Date: December 16, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Senior Scientist
Laboratoire de Physique de la Matière Condensée (CNRS)
École Polytechnique, France
We consider the diffusion-weighted signal at strong magnetic field gradients of a finite duration. In this so-called localization regime, the magnetization is dephased everywhere except near the boundaries, making the signal sensitive to the microstructure such as cell walls. We review conventional approaches to diffusion MRI, including the cumulant expansion and diffusion-diffraction, discuss why they fail in the localization regime, and develop the physical intuition for this unconventional case that can be reached on high-performance human and preclinical scanners. Technically, we reveal how the characteristic stretched-exponential signal decay emerges at strong gradients from spectral properties of the underlying Bloch-Torrey operator. The recent theoretical and experimental advances will be discussed from the point of revising some common beliefs and practices in the field.
Date: December 11, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Professor of Radiology, Biomedical Engineering, Electric and Computer Engineering, and Computer Science
Renaissance School of Medicine
Stony Brook University
Computed tomography (CT) enables noninvasive in vivo visualization of tissues’ anatomy and functionality. X-rays with a specific energy interact with tissue and generate a particular image contrast relative to that of water. Thus, X-ray energy spectrum plays an important role in CT imaging. Because the lower-energy X-rays are absorbed more readily by body tissues and do not contribute to image formation, CT imaging uses the higher-energy portion of the spectrum. Thus, CT image reconstruction faces an intrinsically contrast-limited challenge. While injection of contrast media could enhance CT images, it is not the topic of this talk. This presentation will focus on contrast enhancement (equivalent to energy-resolved CT at low energy levels) via spectral CT (dual-energy or photon-counting CT) reconstruction, which is essentially low-dose CT reconstruction. Bayesian inference for low-dose/spectral CT reconstruction aims to incorporate prior knowledge into fully data-driven maximum-likelihood solution, where prior knowledge about the body tissues can be learned from available diagnostic CT images. This presentation will further explore the relationship between the enhanced image contrasts and in vivo tissue biological characteristics for machine learning diagnosis and early detection of cancers.
Date: December 4, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Assistant Professor
Department of Radiology
NYU Grossman School of Medicine
NYU Langone Health
The NIH K99/R00 Pathway to Independence Award provides a unique opportunity for postdoctoral researchers to secure funding and transition to independent faculty positions. However, the application process can be challenging, with specific requirements, competitive standards, and strategies that can make or break your application. This talk will cover the basics of the K99/R00 program, and how it supports the transition from postdoc to faculty, as well as key components of the application and tips for writing. Whether you’re actively preparing a K99/R00 application or just exploring your funding options, this talk will provide valuable insights and practical tips to demystify this career award.
Date: November 20, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Postdoctoral Fellow
Section on Plasticity and Imaging of the Nervous System (SPINS)
National Institute of Neurological Disorders and Stroke
National Institutes of Health
Gene-expression reporter systems, such as green fluorescent protein, have been instrumental to understanding biological processes in living organisms at organ system, tissue, cell, and molecular scales. More than 30 years of work on developing MRI-visible gene-expression reporter systems has resulted in a variety of clever application-specific methods. However, these techniques have not yet been widely adopted, so a general-purpose expression reporter is still required. In this talk, we will demonstrate that the manganese ion transporter Zip14 is an in vivo MRI-visible, flexible, and robust gene-expression reporter to meet this need.
Date: November 13, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Functional MRI can provide in vivo biomarkers that facilitate the differential diagnosis of breast cancer and the assessment of response to therapy. Non-invasive imaging techniques such as diffusion MRI can provide useful information about tumour cellularity, however image quality is limited due to image acquisition methods. Advanced acquisition strategies (such as multiplexed sensitivity encoding, or MUSE) can overcome these limitations. Dynamic contrast-enhanced MRI (DCE-MRI) can provide information about tumour vascularity and has been used in the prediction of pathological complete response to neoadjuvant chemotherapy, particularly through the use of radiomics and deep learning approaches.
While lung function tests are the gold standard for the assessment of progression in lung diseases, they suffer from measurement variability. CT-derived imaging biomarkers can provide objective structural measurements of disease that when used in combination with functional measurements can predict progression and measure response to treatment. Developments in deep learning segmentation algorithms have allowed for fast and accurate automated segmentation of tissues and structures on lung CT scans.
Gabrielle Baxter completed her PhD and first post-doctorate in the department of radiology at the University of Cambridge, investigating the use of diffusion MRI, dynamic contrast-enhanced MRI, and 23Na-MRI in breast cancer. Most recently, she was a research fellow at the Centre for Medical Image Computing in the department of computer science at University College London, where her work focused on the development of CT image-derived biomarkers of lung disease progression and the use of deep learning algorithms for the segmentation of airways, vessels, fat, and muscle.
Date: October 30, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Doctoral Candidate
Neuroscience
University of Western Ontario
The hippocampus is a widely studied yet enigmatic archicortical region which serves multiple cognitive functions. Part of its mystery arises from the difficulty in studying its structure non-invasively. Irrespective of any hypothesis, past and present research has generally focused on the macro level of hippocampal structure, including volume and thickness. While sufficient for some questions, such measures are coarse and generally immutable to different properties of the intrahippocampal gray matter. This includes components such as glial cells, neurites, soma and other micron-scale structures (i.e. microstructure). Such microstructure is responsible for the computations which engender hippocampal function and are of critical importance in both health and disease. Diffusion MRI (dMRI) is one technique which can provide sensitivity to micron-scale structures non-invasively. This talk will present the current landscape, difficulties, and future perspectives of dMRI applied to the hippocampus to understand its microstructure in health, disease, and development. Overall, it looks to answer three questions: why care, what we currently know, and what we hope to know in the future.
Bradley Karat is a PhD candidate in neuroscience at the University of Western Ontario. He currently studies all things MRI and hippocampus, with a particular emphasis on understanding the micron-scale structures of the hippocampus non-invasively including glia, neurites, and soma. In particular, his work focuses on applying/developing novel diffusion MRI methods to improve characterization of hippocampal microstructure in health, disease, and development. With such microstructure characterization, Bradley hopes to gain insight into the eclectic function of the hippocampus and its substructures.
Date: October 16, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Associate Professor
Department of Biomedical Engineering, Tel Aviv University
Sagol School of Neuroscience, Tel Aviv University
Adjunct Assistant Professor
Department of Radiology, NYU Grossman School of Medicine
Myelin is one of the key constitutes of the central nervous system and is involved in numerous developmental and neuropathological processes. Noninvasive assessment of myelin content and integrity, however, is highly challenging with no gold standard available to date. Multi-compartment (mc) analysis of MRI signals, and specifically T2 relaxation times (mcT2), is the most common and efficient approach for quantifying myelin in vivo. The approach is based on separating the signal within each voxel into a series of signals, each originating from a distinct cellular compartment. The fast-relaxing T2 component is, in this case, associated with water residing between myelin sheaths, and provide an indirect measure of myelin content. Notwithstanding its popularity, this approach is highly ill-posed due to large ambiguities in the multi-T2 space and the low SNR that characterizes MRI signals. In this talk I will present a new data-driven approach to mcT2 analysis, which harnesses information from the entire white matter and the power of statistics to identify tissue-specific mcT2 motifs, prior to deconvolving the local signal at each voxel. This stabilizes the process of myelin quantification, and can improve the analysis of microstructural tissue compartmentation in general. Validations will be presented using computer simulations, a unique multicomponent phantom design, mice models of demyelination, as well as healthy subjects and people with multiple sclerosis.
Date: October 9, 2024 at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Research Fellow
Department of Radiology
Massachusetts General Hospital
Harvard Medical School
Microstructure imaging faces several challenges in neuroscientific and clinical applications, including long acquisition and computation times, degeneracies, and biases in parameter estimation. In this talk, I will present some of our efforts aimed at addressing these challenges. First, I will present how we use patch-based convolutional neural networks to accelerate data acquisition, focusing on the estimation of fiber orientation and microstructural properties. Next, I will demonstrate combined diffusion-relaxometry methods for more specific quantification of brain tissue microstructure and composition. I will introduce Microstructure.jl, an open-source toolbox I am actively developing in the Julia language, which unifies these methods into a cohesive framework for parameter estimation and uncertainty quantification across various biophysical models. Finally, I will showcase how these advanced techniques can capture the dynamic microstructural changes during brain development and enhance our understanding of the developing brain.
Date: October 2, 2024 at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Instructor
The Richard M. Lucas Center for Imaging
Department of Radiology
Stanford University
Diffusion MRI is an important imaging tool for studying brain microstructure by detecting the random Brownian motion of water molecules. However, current diffusion MRI methods are confounded by several technical challenges for precise detection of brain microstructure. For example, the widely used diffusion MRI acquisition method, single-shot echo planar imaging (ss-EPI), is known to have a typically low resolution, relatively low SNR, and geometric distortions. The basic diffusion MRI sequence, pulsed gradient spin echo (PGSE), may only provide limited specificity for brain microstructure detection. In this presentation, I will first review how diffusion MRI can be used to detect brain microstructure. Then I will introduce our recent work on advanced diffusion encoding and acquisition and how they affect brain microstructure mapping. Finally, I will discuss further technical development and potential clinical translation of new diffusion MRI techniques.
Date: September 25, 2024 at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Assistant Professor of Radiology
NYU Langone Health
This presentation reports preliminary results from an on-going NIH R01 research project related to sodium MRI and Alzheimer’s disease (AD). Cerebrospinal fluid (CSF) clearance pathway (e.g., glymphatic system) might be disrupted in AD. Recent studies on mice showed impairment of CSF clearance pathway that led to a 70% decrease in amyloid beta (Aβ) clearance but sleep enhanced CSF flow and increased A𝛽 clearance by 100%. However, it is unknown whether these negative and positive impacts exist in humans due to lack of adequate non-invasive techniques for the study. In this project we proposed two unique techniques, i.e., dynamic sodium MRI and ultrashort echo time (UTE) proton MRI, to determine whether CSF clearance is enhanced during sleep, degenerated in aging, and disrupted in AD. Instead of studying perivascular space—a popular research-targeting region, this project was able to investigate full life cycle of CSF simultaneously, including the production at choroid plexus, the bulk flow in brain parenchyma, and the drainage at arachnoid villi. The overarching goal is to understand the changes of CSF clearance in aging and in AD. The outcomes will generate highly-desired knowledge about degeneration of CSF clearance and help develop effective interventions to AD.
Date: September 16, 2024 at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Professor of Radiology and Neurology
Wayne State University
STAGE is a rapid multi-contrast imaging technique that can provide not only standard T1 and T2 like contrasts but also proton spin density weighted images, T2* weighted images, SWI, R2* maps, QSM maps, water content maps, circle of Willis MRA, auto segmentation of white matter, gray matter and cerebrospinal fluid and simulated FLAIR in 3 to 6 minutes. Coupled with a new denoising algorithm called CROWN, STAGE can be used to create a new series of homogeneous images that have been corrected for both radiofrequency transmit and receive inhomogeneities. During the last few years, we have focused on measuring iron content and neuromelanin (NM) in the substantia nigra (SN) for comparing idiopathic Parkinson’s disease (PD) with healthy controls and patients with other movement disorders. We have found that the volume of NM, the iron content of the SN, volume of the SN and the N1 sign can together provide an area under the curve (AUC) of 95% in distinguishing PD from healthy controls. We have developed a template of the midbrain to allow for automatic detection and quantification of these properties. During the process, we used tSWI to enhance the N1 sign visibility. Our data have been acquired using STAGE which is a rapid, quantitative, multi-contrast data collection and processing that is vendor agnostic. As such we have created a protocol that can be used for PD studies globally.
More recently, we have been using fast low flip angle multi-echo (FLAME) spin density weighted imaging to map the white matter fiber tracts from the brainstem up to the thalamus in vivo. Although tractography from diffusion tensor imaging has been commonly used to map the WM fiber tracts connectivity, it is difficult to differentiate the complex WM tracts anatomically. With a clear delineation of major fiber tracts, it may be possible to use structurally constrained DTI fiber tracking to improve their visualization. Using the FLAME approach, we have been able to map out all the major fiber tracts in a reasonably short scan time of 10 minutes with 0.67×0.67×1.34 mm3 resolution at 3T.
Date: September 11, 2024 at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Recognition is a fundamental cognitive function during which the brain projects perceptual templates learnt from past experiences onto the current sensory input. A key factor in this process is how much the brain weights prior knowledge versus the sensory input. This weighting is known to vary between individuals and may contribute to hallucinations in mental illnesses when prior knowledge is weighted to a pathologically high degree. By manipulating human subjects’ ability to use prior knowledge to recognize stimuli during fMRI and intracranial EEG neural recording, we can examine the underlying neural mechanisms of prior knowledge deployment from distinct sources of prior knowledge. By training artificial neural networks to perform similar image recognition tasks, we can identify plausible computations the human brain may use to achieve the same. Specifically, this project examines visual recognition aided by prior knowledge derived from lifelong learning as well as one-shot learning. This talk will present early results and plans to integrate data from multiple experiments.
Jonathan Shor is a sixth-year PhD student advised by Biyu He, associate professor of neurology, neuroscience and physiology, and radiology. After receiving his BS in computer science, Jonathan spent 10 years in the private sector before returning to academia, earning an MS in computer science at Columbia University before joining the Vilcek Institute. He is interested in using neuroimaging and computational techniques to identify the neural correlates of conscious perception.
Date: August 20 2024, at 11:00 a.m.
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Director, Center for Brain and Health
Associate Professor of Psychology, NYU Abu Dhabi
Global Network Associate Professor of Psychology, New York University
The visual system develops abnormally when visual input is absent or degraded early in life. Restoration of the visual input past 7 or 8 years of age is generally thought to have limited benefit because the visual system will lack sufficient plasticity to adapt to and utilize the information from the eyes. Recent evidence, however, shows that congenitally blind adolescents can recover both low-level and higher-level visual function following surgery. In this talk, I will discuss recent work in our lab that links changes in behavioral performance to longitudinal changes in white matter integrity in congenitally blind patients with dense bilateral cataracts. Our results suggest that sufficient plasticity remains in adolescence to partially overcome abnormal visual development and help localize the sites of neural change underlying sight recovery.
Date: August 12 2024, at noon
Location: 660 1ST AVE FL 3 and via Webex
PhD Candidate
University of Florida College of Medicine
Multiple sclerosis (MS) is a neuroimmune disorder characterized by demyelination and neurodegeneration, leading to cognitive and motor impairments. This study investigates the effects of two MS-modifying medications, interferon beta and dimethyl fumarate, on functional brain connectivity using graph theoretical network analysis. We recruited 107 relapsing-remitting MS (RRMS) patients and 62 healthy controls, collecting resting-state functional MRI data to construct brain networks based on regional blood-oxygen-level-dependent signals. The study consisted of two parts: region-based and connection-based analysis. In the region-based analysis, we assessed graph theoretical measures, including degree centrality, nodal efficiency, and betweenness centrality, to evaluate alterations in brain networks and to explore hubs as highly connected regions in healthy controls and patient groups under different medications. The connection-based analysis employed an edge-centric approach to examine metrics such as entropy, co-fluctuation, and edge strength. Results from the region-based analysis showed differences in network metrics, particularly in the default mode, dorsal attention, and salience networks, among patients on different medications. The connection-based analysis revealed changes in motor, sensory, visual, and attention networks, alongside variations in the brain network strength. Entropy and changes in edge functional connectivity (eFC) revealed distinct effects of each medication. Interferon beta caused hemisphere-wide alterations in the somatomotor network, with opposite changes in each hemisphere. Conversely, dimethyl fumarate’s effect was marked by decreased entropy in the left visual and right dorsal attention networks, indicating reduced diversity in network connectivity. The study highlights potential biomarkers for the treatment effect and demonstrates the nuanced effects of interferon beta and dimethyl fumarate on brain function in patients with MS.
Sara Hejazi is a brain network scientist with a diverse background in engineering and neuroscience. She received her PhD from the engineering college at the University of Central Florida in May 2024. Her doctoral research utilized brain network analysis to investigate changes in brain connectivity in patients with multiple sclerosis (MS) using fMRI data. During her internship at the Mayo Clinic’s department of artificial intelligence and informatics from summer 2022 to spring 2023, she contributed to a project involving network analysis aimed at studying the development of mild cognitive impairment (MCI) and its progression to dementia. In the summer of 2023, she joined the University of Florida College of Medicine to further expand her expertise in brain network analysis in preclinical research, specifically focusing on traumatic brain injury (TBI).
Date: August 5 2024, at 10:00 a.m.
Location: 227 E 30 ST FL 7 RM 717 and via Zoom
PhD Candidate
Institut des Neurosciences des Systèmes
Aix Marseille Université
Institut National de la Santé et de la Recherche Médicale (INSERM)
Epileptogenic foci often organize into networks, making the diagnosis of focal epilepsy challenging due to the spontaneous nature of the disease. While triggering seizures with intra-cranial stimulation aids in diagnosis, concerns about invasiveness drive the research for non-invasive alternatives. In this talk, Chloé will highlight the potential of temporal interference (TI) stimulation in diagnosing refractory epilepsy by identifying epileptogenic zone networks (EZN) in silico. Crucial model parameters, linked to seizure onset, are inferred from patient-specific brain models to quantify brain regions’ epileptogenicity and diagnose the EZN.
Chloé Duprat is a PhD candidate at the Institut des Neurosciences des Systèmes at Aix Marseille Université. She holds a master’s degree in mechanical and biomedical engineering and in computational neuroscience and neuro-engineering. Her work primarily focuses on dynamic modeling and neural stimulation modeling in patient-specific whole-brain models.
Date: July 17 2024, at noon
Location: 660 1ST AVE FL 3 and via Webex
PhD Candidate
Department of Imaging Physics, Magnetic Resonance Systems Lab
Faculty of Applied Sciences
Delft University of Technology
T1ρ mapping is emerging as a promising contrast-free alternative to LGE imaging for myocardial tissue characterization. However, the high sensitivity of conventional spin-lock preparations to B0 and B1+ inhomogeneities renders it ineffective at high field strengths. Adiabatic spin-lock preparations, consisting of trains of hyperbolic-secant pulses, can overcome this limitation. During this talk I will discuss how we deigned and optimized adiabatic T1ρ preparations for in vivo mapping at 3T. I will compare the results of adiabatic T1ρ mapping with conventional non-adiabatic techniques and show how adiabatic spin-locks yielded improved precision and reproducibility. Results from a small cohort of patients indicated that adiabatic T1ρ mapping could potentially be used to discriminate between scar and healthy myocardium. Finally, I will discuss how we used a combination of selective and non-selective adiabatic RF pulses to achieve robust adiabatic T1ρ maps with dark-blood contrast.
Chiara holds a BSc in biomedical engineering from Politecnico di Milano and a double MSc in biomedical engineering from Politecnico di Milano and Politecnico di Torino, with honors. She completed her master’s studies with a thesis project on image filtering and compression for super high frame rate electron microscopy at Lawrence Berkeley National Laboratory. She is currently a PhD candidate at the Magnetic Resonance Systems Lab in the department of imaging physics at TU Delft and expects to graduate in September. During her PhD Chiara has worked on designing novel RF preparations for robust spin-lock imaging at 3T, with a special focus on cardiac MR applications.
Date: June 17 2024, at 11:30 a.m.
Location: 227 E 30TH ST FL 1 RM 120 and via Webex
Assistant Professor of Biomedical Informatics
Harvard Medical School
Accurate interpretation of medical images is crucial for disease diagnosis and treatment, and AI has the potential to minimize errors, reduce delays, and improve accessibility. The focal point of this presentation lies in a grand ambition: the development of ‘Generalist Medical AI’ systems that can closely resemble doctors in their ability to reason through a wide range of medical tasks, incorporate multiple data modalities, and communicate in natural language. Starting with pioneering algorithms that have already demonstrated their potential in diagnosing diseases from chest X-rays or electrocardiograms, matching the proficiency of expert radiologists and cardiologists, I will delve into the core challenges and advancements in the field. The discussion will navigate towards the topic of label-efficient AI models: with a scarcity of meticulously annotated data in healthcare, the development of AI systems capable of learning effectively from limited labels has become a key concern. In this vein, I’ll delve into how the innovative use of self-supervision and pre-training methods has led to algorithmic advancements that can perform high-level diagnostic tasks using significantly less annotated data. Additionally, I will talk about initiatives in data curation, human-AI collaboration, and the creation of open benchmarks to evaluate the generalizability of medical AI algorithms. In sum, this talk aims to deliver a comprehensive picture of the state of ‘Generalist Medical AI,’ the advancements made, the challenges faced, and the prospects lying ahead.
Date: June 12 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Webex
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Contrast-enhanced T1-weighted imaging is an important component of many clinical protocols, such as for the detection of brain tumors and metastases. A widely used acquisition scheme is the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence, which depicts both enhancing lesions and blood vessels with bright signal intensity. As an alternative, a novel family of sequences has been recently proposed, named T1 Relaxation-Enhanced Steady-State (T1RESS), which suppress the contrast of background tissue and signal of flowing blood, resulting in improved conspicuity of enhancing lesions. The improved sensitivity is especially valuable for detecting small lesions, which can have a major impact on the prognosis of a patient. In this work, radial stack-of-stars T1RESS sequences were developed, which offer improved motion robustness and enable advanced reconstructions such as GRASP for dynamic imaging. Three sequence variants were introduced, including (a) balanced T1RESS, which can potentially be used at low field (0.55T) for high-SNR T1-weighted imaging; (b) unbalanced T1RESS-FISP, which suppresses the signal of blood vessels, and (c) unbalanced T1RESS-PSIF, which provides dark-blood imaging. To broaden the clinical applicability, the combination with GRASP reconstruction, DIXON fat/water separation, and 1D GRAPPA acceleration in the Cartesian direction of the stack-of-stars geometry were implemented. The radial T1RESS sequences have been tested for brain imaging and applications in the body in healthy volunteers and patients.
Ruoxun Zi is a fourth-year graduate student in the Vilcek Institute’s Biomedical Imaging and Technology training program working with Kai Tobias Block, PhD, and Riccardo Lattanzi, PhD. She holds an MS in biomedical engineering from Johns Hopkins University.
Date: June 5 2024, at noon
Location: 227 E 30TH ST FL 7 RM 718 and via Zoom
Lewis Bernard Professor of Natural Science
Professor, Chemistry, Princeton Materials Institute
Princeton University
The study of hyperuniform states of matter is an emerging multidisciplinary field, influencing and linking developments across the physical sciences, mathematics and biology [1,2]. Hyperuniform many-particle systems in d-dimensional Euclidean space are characterized by an unusual suppression of density fluctuations at large lengths scales.
The hyperuniformity concept generalizes the traditional notion of long-range order, and provides a unified theoretical framework to categorize crystals, quasicrystals and exotic disordered systems. Disordered hyperuniform many-particle systems can be regarded to be new states of disordered matter in that they behave more like crystals or quasicrystals in the manner in which they suppress large-scale density fluctuations, and yet are also like liquids and glasses because they are statistically isotropic structures with no Bragg peaks.
I will provide an overview of the hyperuniformity concept and its generalizations. Subsequently, I will discuss the diffusion spreadability S(t), which is a measure of the spreadability of diffusion information as a function of time.
Exact formulas for the spreadability in any Euclidean space dimension are derived in terms of two-point statistics that characterize the microstructure.
Further, closed-form general formulas are derived for the short- , intermediate- and long-time behaviors of S(t) in terms of crucial small-, intermediate- and large-scale structural information, respectively. The long-time behavior of S(t) enables one to distinguish the entire spectrum of translationally invariant microstructures that span from hyperuniform to nonhyperuniform media.
For hyperuniform media, disordered or not, the “excess spreadability”, S(∞) – S(t), decays to its long-time behavior exponentially faster than that of any nonhyperuniform medium, the slowest being “antihyperuniform media”. The stealthy hyperuniform class is described by an excess spreadability with the fastest decay rate among all translationally invariant structures.
We establish remarkable connections between the spreadability and problems in discrete geometry, NMR pulsed field gradient spin-echo amplitude as well as diffusion MRI measurements.
REFERENCES
Date: May 22 2024, at noon
Location: 227 E 30TH ST FL 7 RM 718 and via Zoom
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue. However, existing deep learning models analyze each modality independently, missing opportunities to integrate information across imaging modalities and time. In this study, we present Multi-modal Transformer (MMT), a neural network that utilizes mammography and ultrasound synergistically, to identify patients who currently have cancer and estimate the risk of future cancer for patients who are currently cancer-free. MMT aggregates multi-modal data through self-attention and tracks temporal tissue changes by comparing current exams to prior imaging. Trained on 1.3 million exams, MMT achieves an AUROC of 0.943 in detecting existing cancers, surpassing strong uni-modal baselines. For 5-year risk prediction, MMT attains an AUROC of 0.826, outperforming prior mammography-based risk models. Our research highlights the value of multi-modal and longitudinal imaging in cancer diagnosis and risk stratification.
Date: May 16 2024, at noon
Location: 227 E 30TH ST FL 7 RM 718 and via Zoom
Associate Professor of Clinical Neurology
Weill Cornell Multiple Sclerosis Center
Weill Cornell Medical College
Inflammation of the central nervous system (CNS), driven by the innate immune system, plays a crucial role in the pathophysiology of multiple sclerosis (MS). Critical cell types involved in this process include CNS resident microglia and blood-derived macrophages. Imaging methods such as MRI and PET can be used to assess CNS inflammation in MS, and we will discuss our work focusing on these two approaches.
In measuring innate immune activity via MRI, we observe that chronic CNS inflammation in MS lesions is maintained in part by iron-laden pro-inflammatory microglia/macrophages at the rim of chronic active lesions (CALs). These paramagnetic rim lesions (PRLs) are believed to contribute to a more aggressive phenotype of the disease and thus represent a novel target for treatment to reduce disease progression in MS. Utilizing quantitative susceptibility mapping (QSM) to measure PRLs, our group has concentrated on creating tools to identify and quantify lesion-based chronic innate immune activity. We have been investigating the impact of chronic lesion-based inflammation on the disease course. Moreover, we have expanded our work to propose a novel application of QSM to quantify the inflammatory trajectory within PRLs, providing a new target for treatment in MS with either current or novel immune modulators.
Regarding the measurement of chronic inflammation via PET, [11C]-PK11195-PET (PK-PET) is a first-generation ligand that binds to the 18 kDa translocator protein (TSPO), expressed on the outer mitochondrial membrane of activated myeloid cells, and it has been used to demonstrate increased innate immune activity throughout the brains of MS patients. We have employed PK-PET to validate in vivo that PRLs on QSM exhibit higher inflammation than rimless lesions. Newer generation TSPO ligands, with enhanced specificity and brain penetration compared to PK-PET, which may enhance PET’s ability to identify and compare inflammatory activity across individual chronic lesions. We are currently focusing our research on a second-generation TSPO ligand, [11C]-DPA 713. To circumvent the challenges of arterial sampling, we have developed a supervised clustering algorithm (SVCA). This algorithm serves as a reliable non-invasive method for quantifying this ligand’s presence. Additionally, we are applying this innovative technique to investigate the dynamics of individual MS lesions
Date: April 29, 2024, at 11:00 a.m.
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Research Professor of Radiology
Perelman School of Medicine
University of Pennsylvania
Cerebral cortical neural architecture is an important biomarker for prognosis and diagnosis of a variety of brain disorders. At present, assessing soma and neurite (axons and dendrites projecting from cell body) architecture in the cerebral cortex requires neuropathology. However, neuropathology is invasive and provides only local neuronal architecture information. Diffusion MRI (dMRI) has been the method of choice for noninvasively measuring brain microstructure. Cerebral cortical architecture is complicated consisting of somas of neurons and glia as well as neurites. In this talk, I will introduce our efforts towards developing dMRI-based techniques to accurately and reproducibly measure both soma and neurite densities in complicated cerebral cortical architecture by harnessing both a “top-down” and a “bottom-up” approach. Using the “top-down” approach, we modeled the complex cellular architecture of the cerebral cortex by establishing the non-Gaussian dMRI signal modeling for measuring the cortical micro-architecture based on theoretical modelling and diffusion simulations. In the “bottom-up” approach, we quantified the cortical micro-architecture using deep learning by mapping dMRI signal to ground-truth.
Date: April 24, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Doctoral Candidate
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Early detection and treatment of Alzheimer’s disease (AD) and related disorders is paramount in the prevention of neuronal degeneration and dementia. AD is characterized by a long preclinical stage noted by the deposition of amyloid plaques and tau tangles, followed by a destructive cascade of neurodegeneration typically beginning in the medial temporal lobe, leading to mild cognitive impairment and progression to dementia. However, only 40% of patients with preclinical AD progress to mild cognitive impairment, and only 21% of patients with mild cognitive impairment progress to dementia. Further, current procedures for detecting amyloid and tau deposition are either biofluid (CSF and blood) biomarkers that lack spatial information thus limiting their utility in prognosis, or PET scans which are prohibitively expensive and involve IV injection of radiotracers. Thus, there is a need to develop more accessible, spatially specific biomarkers and to differentiate patients with amyloid and tau depositions who will progress to dementia from those who will not. A promising avenue for early detection of neurodegeneration is subjective cognitive decline (SCD). SCD describes patients who complain of memory difficulties but score normally on cognitive testing. Multicenter studies and meta-analyses show increased rates of dementia in participants with SCD. Many previous studies in SCD have focused on volume and cortical thickness, with inconsistent results. Multimodal MRI provides an opportunity to develop biologically sensitive biomarkers that are less expensive than PET, do not involve IV injection of radiotracers, and provide in vivo spatial information of pathology deposition. While amyloid and tau deposits are necessary for the diagnosis of AD pathology, they are not sufficient for clinical prognosis. Further, their role in the biological pathway leading to the neurodegenerative cascade is unclear. Amyloid in particular correlates poorly with clinical symptoms. Scientific consensus at the 2022 Clinical Trials on Alzheimer’s Disease Conference concluded that it is important to explore other potential therapeutic targets alongside amyloid and tau, although the lack of biomarkers pose a significant barrier. While prior DTI research in SCD is promising for prognostication, studies in the medial temporal lobe and cortical gray matter have been limited, despite their importance in Alzheimer’s disease. Here, we use advanced diffusion MRI preprocessing and modeling to study the microstructure of the medial temporal lobe and cortical gray matter of this group with heightened dementia risk. We present several new potential diffusion MRI biomarkers of SCD.
Date: April 8, 2024, at 12:30 p.m.
Location: 227 E 30TH ST FL 7 RM 717 and via Zoom
Assistant Professor
Technion – Israel Institute of Technology
The advent of deep neural networks (DNNs) has marked considerable breakthroughs in magnetic resonance imaging (MRI) analysis. These state-of-the-art methodologies are increasingly deployed to resolve intricate predicaments in quantitative MRI such as diffusion-weighted MRI, and quantitative cardiac T1 and T2 distribution mapping, delivering superior speed and precision over conventional techniques. Nonetheless, existing challenges such as mitigation of motion artifacts and enhancement of resilience against extremely low signal-to-noise ratios still remain, restricting their clinical utility. To overcome these limitations, we introduce an innovative strategy integrating a physically-primed DNN architecture. This unique architecture embeds the signal decay model directly within the neural network, augmenting the network’s generalization capability and fostering the development of stable algorithms, which, in turn, produce refined predictions. Our advanced methodology reveals extensive potential applications including early assessment of response to neoadjuvant chemotherapy in breast cancer patients, establishing motion-robust quantitative cardiac T1 mapping, and T2 distribution mapping for evaluating inflammation in animal models. The proposed approach opens new vistas for more nuanced and clinically viable solutions in the realm of quantitative MRI analysis, paving the way for enhanced diagnostic precision and patient outcomes.
Date: March 21, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Postdoctoral Fellow
Neuroscience Institute
NYU Langone Health
Astrocytes are non-neuronal glial cells in the central nervous system with myriad functions: they maintain the blood brain barrier, recycle neurotransmitters, and are even thought to be a component of every neuronal synapse. We have found that astrocytes also form gap junctional networks that help neurons survive degeneration. Brain regions connected by astrocytes support one another metabolically, but these connections also make linked regions collectively vulnerable to degenerative stress. Astrocyte networks have never been mapped, a critical need considering that these networks may shape the course of degeneration. Here, we use a combination of novel biomolecular tools, tissue clearing, and light sheet imaging to reveal for the first time the shape, extent, and potential plasticity of astrocytic networks in intact brains.
Date: March 6, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Assistant Professor
Department of Radiology
NYU Grossman School of Medicine
Assistant Professor
Department of Radiology
NYU Grossman School of Medicine
The goal of the MRI4ALL Hackathon 2023, which took place last October around the i2i Workshop, was to create a fully-fledged low-field MRI scanner in just four days and to share all developments as open-source resources. Fifty two scientists from 16 institutions participated in the event and worked in four teams on the construction of the main magnet, gradient coils, RF hardware, and software platform. In this talk, we will discuss how the idea for the hackathon came together, present the created scanner design and components, and summarize our experience from the hackathon week. We will also give a live demo of the scanner, which is currently located at our 22nd Street laboratory.
Date: February 21, 2024, at noon
Location: 227 E 30TH ST FL 1 RM 120 and via Zoom
Doctoral Candidate
Electrical Engineering
NYU Tandon School of Engineering
Low-field magnetic resonance imaging (LFMRI) offers greater accessibility to MR scanners by reduced manufacturing and maintenance costs. However, the signal-to-noise ratio of the acquired images is inherently diminished by the use of low field strengths. The standard technique of averaging multiple acquisitions (to increase SNR) reduces LFMRI accessibility for patients by increasing scan time and cost. Hence, one of the recent trends in LFMRI research focuses on employing advanced image processing techniques to enhance few-average, and even single-average, LFMR image quality and enable greater scanner accessibility.
In this talk, we will cover several strategies for self-supervised learning of deep neural networks (DNNs) for MRI denoising, given only unlabeled noisy data. We begin by reviewing standard observation models for parallel (multi-coil) MRI contaminated with additive white Gaussian noise. We will then cover state-of-the-art loss functions for training DNNs when a single, or potentially multiple, noisy observations of each subject are available for training (ex. SURE, Noise2Noise, Coil2Coil). We use labeled MRI datasets with synthetic degradation to allow for quantitative comparison of the different methods. Finally, we will show applications of these techniques to unlabeled noisy MRI datasets of the lung and the prostate acquired at 0.55T. Complications arising in the translation from synthetic to real-world experiments, such as coil-correlation and noise-level estimation, will be highlighted.
Date: February 14, 2024, at noon
Location: on-site and via Zoom
Postdoctoral Fellow
Department of Radiology
NYU Grossman School of Medicine
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
In this talk we highlight the importance of biophysical modeling of the diffusion MRI (dMRI) signal in order to obtain metrics that are sensitive and specific to brain microstructure. We will focus on the standard model of diffusion in white matter (WM), its validation, parameter estimation, and clinical applications. First, Ricardo will present a comprehensive histological validation of standard-model parameters by characterizing WM microstructure in sham and injured rat brains using 3D electron microscopy and ex vivo dMRI. This validation reveals that the standard model is sensitive and specific to microscopic properties. Next, Ying will discuss several different approaches aimed at resolving the degeneracy issue commonly encountered in parameter estimation using clinically feasible dMRI protocols. He will compare the model constraints of existing methods and an unconstrained, data-driven approach. He will present in vivo data regarding early development, multiple sclerosis, and stroke for validation of these diffusion model parameter estimators.
Date: January 31, 2024, at noon
Location: 227 E 30TH ST FL 7 RM 717 and via Zoom
Research Assistant Professor of Electrical and Computer Engineering
University of Southern California
Magnetic Resonance Imaging (MRI) below 1 Tesla is a recently emerging area, driven by the integration of modern MRI infrastructures (gradient system, RF coils, software, etc.) and the unique physical prosperities of the low magnetic field. These modern low-field systems provide lower susceptibility, improved B0 homogeneities, lower specific absorption rate (SAR), lower acoustic noise, and favorably scaled relaxivities (lower T1 and longer T2/2*). These advances unlock new clinical possibilities, particularly in lung imaging, dynamic imaging, and imaging near metallic implants. In this talk, I will give an overview of research conducted using a prototype whole-body 0.55 T MRI (MAGNETOM Aera, Siemens Healthineers) at the Dynamic Imaging Science Center (DISC) of USC since 2021. Covering applications from head to toe, this talk highlights the promising outcomes and potential clinical implications of this innovative 0.55 T MRI approach in cardiac, lung, body, musculoskeletal imaging, and so on.
Date: January 17, 2024, at 1:00 p.m.
Location: 227 E 30TH ST FL 7 RM 717 and via Zoom
Assistant Professor
Department of Radiology
NYU Grossman School of Medicine
Postdoctoral Fellow
Department of Radiology
NYU Grossman School of Medicine
First, Dr. Asslaender will discuss the connection between longitudinal relaxation and magnetization transfer. He will explain recent advances in the biophysical modeling of these effects, outline implications for our understanding of longitudinal relaxation in biological tissue, such as brain white matter, and touch on new avenues for diagnostic imaging of neurodegenerative diseases such as multiple sclerosis. Advanced biophysical models commonly involve a plethora of parameters, which hampers time-efficient mapping of these parameters. In the second part of the talk, Dr. Flassbeck will discuss approaches to overcome these challenges. He will discuss pulse sequence design and optimization in the realm of MR fingerprinting, discuss the concept of the hybrid state, and touch on image reconstruction of highly undersampled dynamic imaging.
Date: January 16, 2024, at noon
Location: 227 E 30TH ST FL 7 RM 717 and via Zoom
Doctoral Candidate
Auckland Bioengineering Institute
University of Auckland
Cerebral palsy (CP) is a neuromusculoskeletal condition arising from a neural lesion of the central nervous system which occurs before, during or soon after birth. The neural lesion leads to progressive muscular degeneration making the condition the most common cause of physical disability in paediatric populations. MRI and Diffusion Tensor Imaging (DTI) provides great potential for an in vivo assessment of muscle structural and architectural properties of lower-limb muscles. This allows for an understanding of the structure-function relationship in lower-limb muscles. Previous research has investigated structural and architectural properties of lower-limb muscles including muscle volume and pennation angle in paediatric populations with CP. In this research, anatomical MRI was used to extract volumes, i.e. structures, of lower-limb muscles. Additionally, DTI was used to generate voxel-based fibre orientations based on the diffusion properties of water molecules within lower-limb muscles, and a deterministic tracking algorithm was used to reconstruct 3D muscle architecture. Structure and architecture datasets were combined to develop longitudinal, statistical models of lower-limb muscles morphology and 3D fibre orientations. The developed models characterised the dominant variations of lower-limb muscles over a specific period of time in paediatric populations with CP and healthy counterparts. The developed models offered a longitudinal, quantitative analysis of muscle structure and architecture providing insights into the progression of muscle degeneration in paediatric populations with CP, which may assist in the design of targeted clinical interventions for motor dysfunctions associated with this condition.
Date: January 16, 2024, at 10:00 a.m.
Location: 227 E 30TH ST FL 1 and via Zoom
Professor and chair of the biomedical engineering undergraduate program
College of Biomedical Engineering and Instrument Science
Zhejiang University
Recent development of time-dependent diffusion MRI (TDDMRI) has demonstrated unique advantages in depicting cellular microstructures by characterizing the time-dependence of restricted water diffusion. Previous simulation and animal studies have shown that TDDMRI is sensitive to microscopic pathology in tumors, yet clinical translation of this technique has been challenging due to the hardware requirement for high gradient strength. Nevertheless, initial tests in human studies are emerging. This talk will first introduce TDDMRI, from theory to animal and human studies, and then describe our recent endeavor in developing new pulse sequences for clinical translation of TDDMRI. I will then talk about our clinical studies in prostate cancer, glioblastoma, and breast cancer, which showed promising clinical value. Last, I will also touch upon our recent work on developing an ultra-high-gradient system for better potential of TDDMRI.
Date: January 10, 2024, at noon
Location: 227 E 30TH ST FL 1 and via Zoom
Doctoral Candidate
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
The choroid plexus (ChP), a highly vascularized structure in the brain ventricular system, is increasingly recognized for its vital role in cerebrospinal fluid (CSF) hemostasis and waste clearance, particularly in relation to aging and dementia. While some imaging studies have investigated its volumetric changes, the majority of ChP studies have relied on histopathological approach. Limited MRI research has been conducted on age-related vascular degeneration, primarily due to resolution and tissue contrast constraints. This presentation will focus on the in vivo exploration of vascular aging in the ChP utilizing multimodal MRI data, including USPIO-enhanced susceptibility-sensitive imaging on 7T as well as dynamic-contrast enhanced (DCE) and arterial spin labeling (ASL) perfusion MRI on 3T; These modalities allow for detailed characterization of both vascular anatomical and blood flow alterations in normal aging process. We observed significant age-related vascular degenerative changes in three adult lifespan study cohorts. These changes include reduced vascular enhancement and blood flow, accompanied by structural alterations, such as increased ChP stromal volume, cysts formation and changes in water mean diffusivity on diffusion MRI. Combined, the observed vascular aging in the ChP through MRI may play a role in compromised ChP functions, including CS secretion, filtration, and waste clearance. These in vivo insights are anticipated to enhance our understanding of the ChP’s involvement in cognitive dysfunction.
Date: December 20, 2023, at noon
Location: 227 E 30TH ST FL 1 and via Zoom
MD/PhD Student
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Quantitative MRI techniques spatially resolve biophysical parameters generally by leveraging two steps in tandem: image reconstruction followed by parameter fitting. In this work, we propose improvements to both aspects of this traditional pipeline. Using the Cramér-Rao bound as a figure of merit, we design a computationally efficient low-rank reconstruction method that significantly improves the accuracy and precision of the downstream biophysical parameters. This is combined with a neural network designed to replicate the properties of a minimum variance unbiased estimator. We test our methods for single-compartment T1/T2 MR-fingerprinting in addition to two-pool magnetization transfer imaging in the hybrid state. Combined, our proposed methods facilitate the development, validation, and translation of novel quantitative MRI biomarkers.
Date: December 15, 2023, at 10:00 a.m.
Location: 227 E 30TH ST FL 1 and via Microsoft Teams
Elias A. Zerhouni Professor of Radiology and Radiological Science
Johns Hopkins University School of Medicine
The brain represents two percent of the body weight but consumes 20 percent of the energy budget. Thus the brain’s oxygen supply and metabolism are carefully controlled to maintain its function and health. Aberrant brain oxygen homeostasis is implicated in many brain disorders such as Alzheimer’s disease and cerebrovascular diseases. It is challenging to measure brain oxygen metabolism in vivo, often requiring the injection of radioactive tracers with very short half-life. In this seminar, I will introduce an MRI based technique to measure brain oxygenation and metabolism in rodents and humans across the lifespan, from one day to 90 years old. I will also show potential applications of the technique in brain aging and diseases.
Date: December 14, 2023, at 11:00 a.m.
Location: on-site and via Microsoft Teams
Jim and Sara Anderson Associate Professor of Electrical Engineering
University of Minnesota
Lengthy data acquisition remains a major bottleneck in magnetic resonance imaging (MRI), often necessitating tradeoffs in resolution and signal-to-noise ratio. Thus, reconstruction and acquisition techniques for rapid imaging, noise reduction and improved data acquisition have received great interest. Each of these directions correspond to a specific inverse problem with its own distinct forward operator dictated by the underlying imaging physics.
In this talk, we will describe recent advances that link these inverse problems in MRI through the lens of intelligent physics-driven technologies. We will first focus on physics-driven deep learning (DL) methods for accelerated MRI. In this context, we will overview our pioneering work on self-supervised learning strategies for training such reconstruction algorithms when ground-truth data is not available, which is a common problem in MRI. We will also show how these can be extended to a subject-specific zero-shot setting when a training database cannot be curated. We will then explore state-of-the-art methods for denoising MRI series that utilize random matrix theory based approaches. We will discuss how this strategy can be combined with physics-driven DL reconstruction to provide a synergistic improvement. Finally, we will overview emerging developments for improving radiofrequency pulse design with a focus on improving field inhomogeneity at ultrahigh field strengths.
Date: December 13, 2023, at noon
Location: 227 E 30TH ST FL 1 and via Zoom
Assistant Professor
Department of Applied Mathematics and Computer Science
Technical University of Denmark (DTU)
The talk will focus on the high b-value analysis of brain diffusion MRI data to achieve a robust quantification of white matter axonal properties such as the apparent diffusivities, radius, T2, and signal fraction. However, a common restrictive assumption behind this kind of quantification is the absence of cellular compartments. We will see that by using zonal modeling (a generalization of the spherical variance approach) of the high b-value signal it is possible to account for cell-like contributions and achieve an unbiased quantification of axonal properties. Although not free from its challenges, this unbiased quantification provides the collateral advantage of a better resilience to partial volume effects with gray matter and cerebrospinal fluid. These findings extend beyond current methodologies based on powder averaging, highlighting the importance of unbiased quantification in diffusion MRI.
Date: December 6, 2023, at noon
Location: 227 E 30TH ST FL 1 and via Zoom
PhD Candidate
Electrical Engineering and Computer Sciences
University of California at Berkeley
Motion in MRI scans causes image corruption and remains a barrier to clinical imaging. We propose Beat Pilot Tone (BPT), a simple yet powerful serendipitous system exploitation that turns any MRI receiver chain into a radio frequency (RF) motion sensing system that can operate at arbitrary frequencies (up to several GHz). Our contact-free system can be implemented on any MRI scanner regardless of field strength. Through electromagnetic field simulations and experiments, we explain BPT’s novel mechanism: two or more transmitted RF tones form motion-modulated standing wave patterns that are sensed by the same receiver coil arrays used for MR imaging. These waves are incidentally mixed by intermodulation and digitized simultaneously with the MRI data. BPT achieves an order of magnitude greater sensitivity to motion than other RF methods in detecting and separating common motion types (respiratory, bulk, cardiac, and head motion) in volunteers. Moreover, BPT offers tunable sensitivity to motion based on the transmit frequencies; at microwave frequencies, BPT can detect millimeter-scale vibrations (ballistocardiograms). With multiple antennas and frequency-multiplexing, BPT can operate as a multiple-input multiple-output (MIMO) system. Preliminary experiments have demonstrated the utility of BPT for retrospective head motion correction. BPT significantly expands the capability of any MRI system, paving the way toward multi-modality, motion-robust, and simultaneous RF and MR imaging.
Date: November 30, 2023, at noon
Location: on-site and via Microsoft Teams
PhD Candidate
Radboud University Medical Center
Lymph node metastases significantly influence patient prognosis and treatment decisions. To improve lymph node staging, 3D mGRE imaging is combined with a superparamagnetic iron oxide contrast agent (USPIO) while addressing challenges associated with respiratory motion, ultrahigh field strengths, and T2*-weighted imaging.
Date: August 12 2024, at noon
Location: 660 1ST AVE FL 3 and via Webex
PhD Candidate
University of Florida College of Medicine
Deep generative models use deep neural networks to model complex distributions. Score-based generative models (SGMs) have recently become the de facto method for modeling image distributions. As such, there has been a concerted effort to leverage SGMs to solve inverse problems in medical imaging. In this work we develop methods for the application of SGMs to PET image reconstruction, specifically considering the nuances of the modality. We propose adaptations to sampling methods, MR image guided reconstruction using classifier-free guidance, and acceleration for scaling to fully 3D reconstruction.
Date: November 15, 2023, at noon
Location: on-site and via Microsoft Teams
Vice Chair for Strategy Department of Radiology
NYU Grossman School of Medicine
The talk will describe imaging modalities that can be used to assess bone microstructure and its contribution to bone strength. Studies in anorexia nervosa and obesity as well as studies following caloric restriction and weight gain will be discussed.
October 17, 2023, at noon
Location: 660 1ST AVE FL 3 and via Zoom
Professor and Associate Chair, Basic Science Translational Research
Radiological Sciences Laboratory, Department of Radiology
Stanford University
No abstract was provided for this talk.
Date: September 26, 2023, at noon
Location: on site and via Microsoft Teams
Doctoral Candidate
Department of Bioengineering at Imperial College London
Department of Radiotherapy and Imaging at The Institute of Cancer Research, London
Magnetic resonance fingerprinting (MRF) is a rapid quantitative imaging technique. We compare T1 and T2 measurements from MRF to T1 variable flip angle mapping and T2 multi-echo spin echo mapping in the NIST phantom, 10 healthy volunteers and 17 glioma patients. Our results suggest magnetisation transfer (MT) effects in healthy brain tissue and glioma cause biases between MRF and other mapping methods. MRF’s sensitivity to MT presents an opportunity to characterise brain tumours in more detail.
Date: August 23, 2023 at noon
Location: via Zoom
Clinical Neuroradiologist and Postdoctoral Researcher in Translational MRI
KU Leuven
Leuven, Belgium
This presentation will delve into new tractography methods used for presurgical mapping, specifically focusing on multi-level fiber tractography (MLFT) and constrained spherical deconvolution (CSD) probabilistic tractography. We will outline the unique advantages these techniques offer in a presurgical environment, as demonstrated by two different research studies. This talk will start with an overview of neurosurgery, highlighting the critical role of neuroimaging. We will clarify basic concepts relating to diffusion MRI, fiber tractography (FT), and brain mapping with transcranial magnetic stimulation (TMS) and intraoperative direct electrical brain stimulation (DES). Additionally, we will provide a brief overview of two techniques developed in our lab, which have been integral to the success of the fiber tracking studies under discussion. These innovative methods were essential for the two studies investigating the importance and potential of advanced tractography methods in the field of neurosurgery.
Date: July 31, 2023, at noon
Location: on-site and via Zoom
Associate Professor
The Edmond and Lily Safra Center for Brain Sciences (ELSC)
Hebrew University of Jerusalem
Aging and neurodegeneration are associated with changes in brain tissue at the molecular level, affecting its organization, density, and composition. These changes can be detected using quantitative MRI (qMRI), which provides physical measures that are sensitive to structural alterations. However, a major challenge in brain research is to relate physical estimates to their underlying biological sources.
In this talk, I will highlight approaches for differentiating between changes in the concentration and composition of lipid and iron in the brain. I will first present a biophysical model that stems from the notion of relaxivity, the ability of a certain compound to increase the MR relaxation of the surrounding water proton spins. I will then suggest a phantom system of lipid and iron forms to test the relaxivity approach. Next, I will describe the intrinsic relaxivity of brain tissue in vivo during changes in the aging human brain. Finally, I will compare the in vivo approach to histological characterization of lipids and iron compositions of the brain.
Date: July 11, 2023, at 1:00 p.m.
Location: on-site and via Zoom
Associate Professor in Residence
Department of Radiology and Biomedical Imaging
University of California at San Francisco
MRI at lower field strengths (e.g., 0.55 T or below) has emerged as an exciting area of research in recent years, preseting both new opportunities and unique challenges. One notable advantage of low-field MRI is reduced cost, which is crucial to increasing the accessibility of MRI in healthcare facilities with limited resources. However, the benefits of low-field MRI go beyond cost considerations, and it offers distinct opportunities for certain MRI applications such as shorter T1, reduced metal artifacts, longer T2* times, and higher gadolinium relaxivity. In the meantime, it is also essential to acknowledge that low-field MRI does have certain important limitations, such as a lower signal-to-noise ratio and reduced fat suppression performance. Also, to reduce cost, current 0.55 T scanners have had limited gradient performance.
Last year, UCSF successfully installed a state-of-the-art Siemens 0.55T Free.Max MRI scanner. Since then, our team has been actively investigating its capabilities in body imaging. In this talk, I will provide an overview of our firsthand experience with the Free.Max scanner for body MRI and will also discuss the opportunities and challenges for body imaging at this field strength.
June 21, 2023, at noon
Location: on-site and via Microsoft Teams
Associate Attending Physicist
Memorial Sloan-Kettering Cancer Center
The use of MR to plan, deliver, monitor, and assess the efficacy of radiotherapy is an area of increasing interest and exploration. In the context of personalized treatment, a principal benefit of this modality is the ability to guide adaptive radiation therapy with improved target definition and without the exposure of healthy tissue to ionizing radiation. Recent technical advances in hybrid devices that combine a medical linear accelerator with a diagnostic MRI scanner have enabled such methods of increased treatment precision under real-time guidance and adaptation. These hybrid systems enable daily planning to account for interfraction as well as intrafraction motion using high-resolution, volumetric, real-time monitoring of tumor and organs-at-risk during radiotherapy delivery. They have the potential to account for both complex and systematic changes whether they are due to random changes in organ shape or more systematic changes in tumor volume using both online and offline adaptive radiotherapy. This talk will focus on the current status of such hybrid MR-guided radiotherapy systems and discuss some challenges and potential solutions.
Date: June 13, 2023, at noon
Location: on-site and via Microsoft Teams
PhD Candidate
Doctoral Program in Physics (EDPY)
Ecole Polytechnique Fédérale de Lausanne (EPFL)
Type C hepatic encephalopathy (HE) is a severe neuropsychiatric disease occurring as a consequence of chronic liver disease, for which the prognosis is poor in the absence of liver transplantation. The understanding of biochemical mechanisms underpinning neurological and cognitive dysfunctions is still incomplete. In the first part of this presentation, we will show that diffusion-weighted MR spectroscopy (dMRS) and imaging (dMRI) at 14.1T probed cell-specific changes in metabolite diffusivities in the cerebellum of a rat model of type C HE compared to control rats, as well as an increased intra-neurite and intra-axon water diffusivity after matter-specific biophysical modeling of the diffusion signal in white and gray matter. These results confirm an alteration of cell density and/or of neurite network complexity observed ex vivo by histology and render dMRS a highly valuable tool to probe cell-specific microstructure in vivo. In the second part, a new preclinical 18F-FDG PET methodology to compute quantitative 3D maps of the regional cerebral metabolic rate of glucose (CMRglc) from a labeling steady-state PET image of the brain and an image-derived input function will be presented. This quantitative approach showed its strength in comparisons of groups of animals with divergent physiologies. In vivo, a 50 percent lower brain glucose uptake, concomitant with an increase in brain glutamine and a decrease in the main osmolytes measured by 1H MR spectroscopy, was observed in the hippocampus and in the cerebellum of HE rats, confirming the hypothesis of energy metabolism alteration in HE.
Date: May 17, 2023, at noon
Location: on-site and via Microsoft Teams
Doctoral Candidate
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Early detection and treatment of Alzheimer’s disease (AD) and related disorders is paramount in the prevention of neuronal degeneration and dementia. AD is characterized by a long preclinical stage noted by the deposition of amyloid plaques and tau tangles, followed by a destructive cascade of neurodegeneration typically beginning in the medial temporal lobe, leading to mild cognitive impairment and progression to dementia. However, only 40% of patients with preclinical AD progress to mild cognitive impairment, and only 21% of patients with mild cognitive impairment progress to dementia. Further, current procedures for detecting amyloid and tau deposition are either biofluid (CSF and blood) biomarkers that lack spatial information thus limiting their utility in prognosis, or PET scans which are prohibitively expensive and involve IV injection of radiotracers. Thus, there is a need to develop more accessible, spatially specific biomarkers and to differentiate patients with amyloid and tau depositions who will progress to dementia from those who will not. A promising avenue for early detection of neurodegeneration is subjective cognitive decline (SCD). SCD describes patients who complain of memory difficulties but score normally on cognitive testing. Multicenter studies and meta-analyses show increased rates of dementia in participants with SCD. Many previous studies in SCD have focused on volume and cortical thickness, with inconsistent results. Multimodal MRI provides an opportunity to develop biologically sensitive biomarkers that are less expensive than PET, do not involve IV injection of radiotracers, and provide in vivo spatial information of pathology deposition. While amyloid and tau deposits are necessary for the diagnosis of AD pathology, they are not sufficient for clinical prognosis. Further, their role in the biological pathway leading to the neurodegenerative cascade is unclear. Amyloid in particular correlates poorly with clinical symptoms. Scientific consensus at the 2022 Clinical Trials on Alzheimer’s Disease Conference concluded that it is important to explore other potential therapeutic targets alongside amyloid and tau, although the lack of biomarkers pose a significant barrier. While prior DTI research in SCD is promising for prognostication, studies in the medial temporal lobe and cortical gray matter have been limited, despite their importance in Alzheimer’s disease. Here, we use advanced diffusion MRI preprocessing and modeling to study the microstructure of the medial temporal lobe and cortical gray matter of this group with heightened dementia risk. We present several new potential diffusion MRI biomarkers of SCD.
Date: April 28, 2023, at noon.
Location: on-site and via Microsoft Teams
Assistant Professor
Department of Neurology and Neurological Sciences
Stanford University
Multiple pathological processes interact with brain regions that support episodic memory, resulting in subtle performance differences well before overt clinical impairment. One common pathological pathway is Alzheimer’s disease, and it is established that both hallmark features of this disease are detectable in many older clinically unimpaired adults (amyloid plaques and tau tangles). We have demonstrated heterogeneity in spatial patterns of tau PET signal among clinically unimpaired adults in key cortical nodes relevant to visual associative memory processes (ventral temporal lobe, precuneus, inferior parietal). Along these lines, we find that fMRI metrics of cortical reinstatement during retrieval of associative pairs is reduced in clinically unimpaired adults with genetic risk of Alzheimer’s (APOE4+), and also shows associations with global measures of abnormal tau in the cerebrospinal fluid and focal signal in the ventral temporal lobe. Interestingly, both abnormal tau and cortical reinstatement strength independently predict individual differences in overall memory performance, emphasizing the presence of multiple age-related pathways that influence cortical mechanisms of memory.
Date: April 26, 2023, at noon
Location: on-site and via Microsoft Teams
Postdoctoral Researcher
Radiological Sciences Laboratory
Stanford University
MRI is a powerful imaging tool. However, technical challenges such as long setup and scan time, sensitivity to physiological and bulk motion, and difficulties with horizontal and longitudinal comparison due to its qualitative nature, have significantly hindered MRI from realizing its full potential. In recent years, with the blooming advances in acquisition, reconstruction, and deep learning, push-button MRI has become possible: it will be a short, continuous acquisition producing bulk-motion-robust, physiological-motion-resolved, and quantitative images containing structural and functional information.
In this seminar, I will introduce my work towards push-button MRI. The first part will focus on Multitasking DCE, which enables continuous free-breathing acquisition with 3D flexible anatomical coverage, around 1-millimeter spatial resolution, 1-second temporal resolution (15-60 times higher than clinical DCE protocols), and accurate quantification of tissue microvasculature properties in the presence of physiological motion. This technique has been successfully translated into clinical practice for the differential diagnosis and disease characterization of carotid atherosclerosis, pancreatic cancer, chronic pancreatitis, and breast cancer.
The second part will introduce new advances in echo-planar time-resolved imaging (EPTI), a highly efficient technique for rapid neuroimaging. The advanced version of EPTI—spherical EPTI (sEPTI)—has achieved an additional 1.4x acceleration and improved system robustness, allowing for whole-head T2* and QSM quantification with 1-millimeter isotropic resolution in just 45 seconds. A generalizable navigator is currently being developed for motion and B0 tracking with a latency of approximately 100 milliseconds to achieve B0 and bulk-motion corrected imaging.
In conclusion, the seminar will provide an outlook into the future of MRI. By leveraging the advances in acquisition, reconstruction, and deep learning, MRI will achieve further acceleration, faster reconstruction, richer information in tissue structure and function, and improved scientific and clinical outcomes.
Date: April 19, 2023, at noon
Location: on-site and via Microsoft Teams
Machine Learning Research Engineer
Department of Radiology
NYU Langone Health
Imaging Scientist
Department of Radiology
NYU Langone Health
Mercure is a flexible open-source DICOM orchestration platform developed by the Center for Advanced Imaging Innovation and Research. It offers an intuitive web-based user interface and extensive monitoring options. It can be used for dispatching DICOM studies to different targets based on easily definable routing rules and for processing DICOM series with custom-developed algorithms, such as inference of AI models for medical imaging.
The Center for Advanced Imaging Innovation and Research has deployed several models via Mercure, making them directly available for researchers via Visage. This presentation will cover an overview of Mercure, examples of deployed models currently used by NYU Langone researchers in their PACS workflow, and instructions for deploying your own models via Mercure. There will be a brief introduction to the MONAI Deploy framework and how it can be utilized with Mercure for the rapid deployment of pre-trained open source models. Examples of deployed models will include glioma segmentation, anatomical brain segmentation, and whole body CT image segmentation.-
Date: March 29, 2023, at noon
Location: on-site and via Microsoft Teams
Assistant Professor of Radiology
Biomedical Engineering and Imaging Institute (BMEII)
Icahn School of Medicine at Mount Sinai
Thin skin-wearable devices, sometimes referred to as epidermal electronics, found in many research articles fall short as concept-only representation due to the fundamental discrepancy in the mechanics of thin-film materials and rigid essential components. This talk introduces a set of engineering solutions to overcoming the challenges in manufacture and assembly of epidermal electronics and the soft wearable bioelectronics platform in general. Strategic integration of thin-film electronics with soft elastomers allows the stretchable biopotential electrodes to maintain the conformal contact with the skin while the integrated circuit deforms naturally with the body. The stretchable electrodes with optimized design and structure for intimate skin integration are capable to perform high-fidelity electrophysiology and accurate analysis of the skin’s electrical properties, such as the galvanic skin responses. Moreover, direct integration of small, off-the-shelf chip sensors (e.g., accelerometer, pulse oximeter, and microphone) with a stretchable electronic platform opens the possibility for concurrent monitoring of multiple physiological parameters, while providing researchers with freedom of device placement on the body. Implementation of smartphone applications embedded with real-time classification algorithms demonstrates the feasibility of multifaceted analysis with a high clinical relevance. Finally, results from multiple human studies of various scenarios reveal the true potential of the soft bioelectronics as both a powerful research tool and a game-changer for wearables-enabled digital health.
Date: March 23, 2023, at noon
Location: via Zoom
Doctoral Candidate
Department of Electrical and Computer Engineering
University of Illinois, Chicago
Diffusion-weighted MRI is a powerful medical imaging technology that allows for noninvasive assessment of degenerative spinal cord. In particular, diffusion tensor imaging (DTI) has been widely used in evaluation of mouse spinal cord affected by amyotrophic lateral sclerosis (ALS). However, no significant changes in diffusivity, which can provide environmental information, were found between wild type and mutant groups. To address this issue, a novel ultra-high b-value diffusion-weighted MRI technique was utilized to identify early-stage diffusivity changes caused by ALS in the spinal cord’s degraded microenvironment. This presentation will cover the imaging protocols for both ex vivo and in vivo studies, as well as post-processing techniques using continuous time random walk and multicomponent analysis. The speaker will also briefly discuss representative studies that she assisted with during her graduate research assistantship.
Date: March 22, 2023, at noon
Location: on-site and via Microsoft Teams
Assistant Professor of Radiology
Co-director, Artificial Intelligence and Emerging Technologies concentration at the Graduate School of Biomedical Sciences
Icahn School of Medicine at Mount Sinai
In my talk, I will introduce the motivation behind performing fMRI in the spinal cord at 7 T and the technical challenges that are magnified by this shift. These include the relative paucity of RF coils available for spinal cord MRI at 7 T, increased spatial and temporally-varying B0 inhomogeneity due to proximity to the vertebral column and lungs, and other situations where established methods in brain fMRI need to be re-evaluated and re-optimized for use in the spinal cord. I will then review several technical developments that facilitate spinal cord fMRI at 7 T, including hardware design, dynamic B0 shimming, and collection of GRAPPA autocalibration signal data, and finish with an overview of a thermal pain stimulus task-fMRI study where we applied these methods.
Date: March 21, 2023, at noon
Location: on-site and via Microsoft Teams
Assistant Professor
Department of Radiology
NYU Grossman School of Medicine
Postdoctoral Fellow
Department of Electrical Engineering and Computer Sciences
University of California at Berkeley
Magnetic Resonance Imaging (MRI) is a superb imaging modality, which offers rich information about the human body. However, its clinical use is hindered by the long scan duration and high sensitivity to motion artifacts. Moreover, due to the high complexity of the MRI system, MRI components are commonly designed separately, which leads to sub-optimal performance. Although machine learning (ML) techniques have shown great promise for addressing these limitations, their development is hindered by the scarcity of suitable training data. This seminar will introduce new strategies for developing ML-powered computational frameworks for fast, motion-robust MRI. First, data-related challenges will be discussed; it will be demonstrated that naïve use of open-access medical databases could lead to biased, overly optimistic results. Then, new strategies for rethinking the entire imaging pipeline will be discussed. Two computational frameworks for rapid dynamic (temporal) imaging will be introduced, with end-to-end acquisition-reconstruction design. The first, BladeNet, combines a motion-informative “PROPELLER” sampling technique with a unique ML-based reconstruction network. This framework enables fast, motion-robust, free-breathing imaging, which is highly suitable for pediatric body imaging. The second framework, K-band, addresses challenges in 4D (dynamic-volumetric) MRI by introducing an end-to-end pipeline design, with fast data acquisition and self-supervised reconstruction. This pipeline enables training model-based reconstruction networks using only limited-resolution data, with real-time generalization to high-resolution reconstruction during inference. The seminar will conclude with an outlook to the future of computational medical imaging, focusing on low-coast portable scanners and personalized longitudinal healthcare.
Date: March 16, 2023, at noon
Location: via Webex
Professor of Psychology
University of Pittsburgh
Producing accurate input-output mapping of the human connectome is a great scientific challenge our time and technologies. My team has been pursuing MRI diffusion approaches to making an accurate connectome for 14 years. I have been disappointed at the low accuracy of current methods to be able to reproduce established anatomical connectivity. The diffusion MRI community has not sufficiently recognized that known anatomy shows substantial within-tract-fasciculus migration of small scale axon bundles (0.3 mm diameter) passing at small angles (under 10 degrees) and making sharp turns (270 degrees in 0.1 mm) with punctate areas of crossings within tract. This anatomy poses what I view to be insurmountable challenges for in vivo diffusion human imaging to produce a reasonably accurate (over 90 percent) input-output connectome map. We are developing a new method we call Fasciculus Axon Connective Tissue Multiscale Imaging (FACTMI) using multiscale imaging including MRI at 20-micron resolution and selective 0.1-micron optical Magnify imaging. I believe an accurate connectome can be provided at viable cost and throughput within five years.
I will describe our team effort to achieve the “Pittsburgh early visual system connectome challenge.” Can an accurate (greater than 90 percent hits minus false alarms) map each of 4 fasciculus coding inputs (each eye lateral and nasal visual field) to the 12 (6 layers of lateral geniculate nucleus left and right) outputs matching the established anatomically correct answer published over 50 years ago (Wiesel & Hubel 1966). I will review embryonic development, fasciculation, and neuronal migration, in the context of local guidance molecules and changing topology that we expect observe in the 8 cm from eye to lateral geniculate nucleus (LGN).
With our Max Planck/Stuttgart colleagues, we are a developing harvested tissue slice imaging to deliver high-resolution (20 micron voxel) MRI structural tensor fasciculus following to map the pig and human early optical system. It includes developing CMOS MRI-on-a-chip technology for large parallel-optimized coil arrays within 2.5 mm of the tissue for imaging at 14 T. The 20-micron imaging enables following the walls of fasciculi that are 20-100 microns thick with 4,000 MRI slices from the eye to LGN. We use 0.1-micron Magnify histology to provide counts of axons in each fasciculus along the path. We will follow fasciculus walls to predict the path of fasciculated axons in each of more than 150 fasciculi (0.2 to 0.8 mm diameter sets of ~8,000 axons) per optic nerve. We are testing MRI methods with phantoms that provide ground truth of the paths of millions of taxons (textile axon size tubes; 0.9 micron in diameter) routed in paths to match histology data. We test biological validation in harvested pig tissue at viable scanning time (4-day scans of the optic nerve system at 20-micron voxels). We test the accuracy of the connectome mapping quantification by scoring the ability to predict the number and diameter of the axons at each fasciculus at points along the path from the eye, through the optic chiasm, to layers of LGN based on 20-micron MRI data. We will use deep learning synthetic histology to predict observed histology from low (1 mm) and high-resolution (20 μm) MRI data.
Date: March 15, 2023, at noon
Location: on-site and via Microsoft Teams
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Optoacoustic tomography is a mesoscale volumetric imaging modality which combines the advantages of optical imaging contrast and low acoustic scattering in tissue. In my work, I apply optoacoustic tomography for minimally invasive functional neuroimaging, taking advantage of exogenous contrasts such as near-infrared dyes and genetically encoded calcium indicators as well as endogenous hemodynamic contrast. Each experiment utilizes wavelengths within the near-infrared optical tissue window, at which low light absorption allows optoacoustic imaging at centimeter-scale depths in tissue. In addition to experiments with anesthetized mice, I also developed and implemented the first setup for optoacoustic neuro-tomography in awake, behaving animals.
Date: March 8, 2023, at noon
Location: on-site and via Microsoft Teams
Graduate Student
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Radial MRI sequences have shown significantly higher robustness to motion, enabling patient exams during ongoing motion such as free breathing. In addition, radial sequences offer powerful undersampling properties, especially when combined with compressed sensing, which can be exploited for flexible image contrast manipulation and for highly accelerated DCE-MRI using advanced reconstruction algorithms such as GRASP. In this talk, I will present two novel applications of radial MRI that benefit from its unique sampling properties.
The first application addresses fat suppression at 0.55 T. Low-field MRI systems have gained strong interest due to lower cost. However, the reduction in field strength leads to significant challenges for fat suppression due to the small spectral fat/water distance, which makes conventional fat suppression techniques ineffective. To address this limitation, I will describe an alternative approach for fat suppression using continuous radial acquisition during frequency sweep of an RF saturation pulse, combined with frequency-resolved compressed-sensing reconstruction.
The second application aims at volumetric dynamic MR imaging for functional kinematic assessment of the wrist, which can be useful for evaluating wrist instability. Existing real-time MRI methods are typically either limited to 2D imaging or provide only low temporal resolution and insufficient image quality. To address this challenge, I will present a novel approach for volumetric dynamic wrist examination that assembles 2D real-time data into 3D snapshots using MRI-visible markers on a 3D-printed platform to guide continuous ulnar-radial deviation.
Date: February 15, 2023, at noon
Location: on-site and via Microsoft Teams
Graduate Student
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Prostate cancer is the most diagnosed malignancy and the fourth leading cause of death in men, with more than 80 percent of men developing the condition by the age of 80. MRI has become an increasingly important tool for prostate cancer diagnosis and management, including biopsy guidance. Faster imaging and automated diagnostics may enable more cost-effective workflows and make prostate MRI more widely accessible. To achieve faster imaging, there has been a surge in machine learning based MR reconstruction research. Supervised machine learning based methods for image reconstruction require vast amounts of raw k-space data for model training. The limited availability of raw k-space datasets motivated NYU Langone Health and Meta AI Research (formerly Facebook AI Research) to release the fastMRI dataset in 2020. To our knowledge, fastMRI is the largest public dataset of raw k-space, including knee and brain MRIs, acquired from a clinical population. This resource encourages exploration of multiple fast, pathology informed reconstruction methods by the MR and AI community. To further advance this goal, this study aims to add a biparametric prostate dataset to fastMRI to facilitate the development of machine learning tools to increase the utility of prostate MRI.
Date: February 15, 2023, at noon
Location: on-site and via Microsoft Teams
PhD Candidate
University of California, Berkeley
During MR acquisitions, magnetic susceptibility causes field distortion, a slight Larmor frequency shift and further induces a faster signal decay. Hence, its effects are often treated as an undesired artifact. The phase of a gradient recalled echo signal captures spatial variations of magnetic susceptibilities. By solving a magnetic dipole model using the B0 inhomogeneity field map, we are able to recover the tissue’s local magnetic susceptibility namely quantitative susceptibility mapping (QSM). QSM reflects the local molecular contents and tissue architecture and has shown great potential in research on brain development and neurodegenerative diseases. Common magnetic susceptibility sources in the brain are paramagnetic species such as iron deposition, hemorrhage, and diamagnetic species such as myelin, calcification plaques, beta-amyloid plaques, and tau-protein tangles.
However, what if the paramagnetic and diamagnetic sources co-localize in one voxel? Using the simple dipole model, the phase effect from positive and negative magnetic susceptibilities will cancel out and appear zero value in QSM. This talk will introduce a recently proposed 3-pool signal model DECOMPOSE-QSM that can resolve the susceptibility mixture situation. The multi-echo gradient echo MR signal of one voxel is represented as a summation of signals from 3 voxel pools. The parameters of the signal model are used to construct paramagnetic and diamagnetic component susceptibility maps. The application of this method shows its potential to emphasize the iron overload in basal ganglia for Parkinson’s disease. It also potentially can detect demyelination in an Alzheimer’s disease study. Further, with multi-orientation acquisitions, more coherent susceptibility-based fiber structures are revealed with DECOMPOSE-QSM processed magnetic susceptibility tensor imaging (STI) and high angular resolution susceptibility imaging (HARSI).
Date: January 26, 2023, at noon
Location: on-site and via Microsoft Teams
PhD Candidate
Center for Data Science
New York University
Interpretability is a crucial aspect of deep learning models in medical imaging applications. However, achieving interpretability through fully supervised methods requires a significant amount of human-provided training labels, which can be costly and difficult to scale in large datasets. Furthermore, in many tasks, human-provided training labels are not available, as even experts may not possess the necessary knowledge. In these situations, full supervision is not practical. In this talk, I will present two lines of research on building interpretable deep learning models that can learn from imperfect labels. The first series focuses on a model that can localize cancer without the need for localization labels. We will explore its application in breast cancer screening and the prognosis of COVID-19 patients. The second series centers on learning from imprecise labels. We will examine how this approach enables the model to interpret breast ultrasound and histopathology images.
Date: January 25, 2023, at noon
Location: on-site and via Microsoft Teams
Associate Professor
Department of Chemical and Biological Physics
Weizmann Institute of Science, Israel
Magnetic resonance is unique in its ability to image a wide range of physiological processes noninvasively, using a rich palette of different imaging contrasts—often without the injection of an external agent. At the heart of this ability lies the concept of a pulse sequence: our ability to exquisitely control the quantum-mechanical evolution of nuclear spins using external electromagnetic fields. I will talk about recent work in my group, focusing on the development of new pulse sequences for imaging completely new neurophysiological contrast types, ranging from intracellular viscosity to neurotransmitter dynamics, with far-reaching implications for neuroimaging and medical diagnostics.
Date: January 18, 2023, at noon
Location: on-site and via Microsoft Teams
PhD Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Alzheimer’s disease (AD) studies using established imaging (MRI and PET)- and cerebrospinal fluid (CSF)-based biomarkers have shown that molecular changes begin years before symptom onset, but mechanisms underlying the hallmark pathological aggregation of amyloid and tau are still unknown. To address this from a 1H-MRS standpoint, studies have used conventional PRESS sequences in cognitively unimpaired populations to examine metabolic dysfunction associated with AD development and progression in otherwise normal-appearing tissue in the posterior cingulate. While there are ex vivo histopathological and in vivo imaging evidence that AD pathological hallmarks are not confined to the posterior cingulate, 1H-MRS interrogation of other regions in cognitively unimpaired elderly has not been done.
The hippocampus, one of the earliest affected regions in AD, is rarely interrogated by 1H-MRS because it is situated in a location with poor B0 homogeneity and large susceptibility effects. In the first part of this presentation, I will show how we applied a recently validated long-TE sLASER sequence, which, with its high bandwidth adiabatic pulses, reduces chemical shift displacement errors (CSDE) inherent to PRESS; and, with its long-TE, minimizes macromolecular contribution to the background signal, improving quantification of metabolites measured from the hippocampus.
Furthermore, other cortical (posterior and isthmus cingulate, precuneus) and subcortical (caudate, putamen, globus pallidus, thalamus) gray matter structures have shown vulnerability to AD neurodegeneration. In the second part of this presentation, I will show how we applied 1H-MRSI to examine spatial characteristics of metabolic dysfunction in these seven gray matter regions (plus one control region, the lateral occipital).
We then tested whether metabolites measured from both 1H-MRS and 1H-MRSI were associated with (1) APOE4 genotype, a risk factor for amyloid burden, (2) CSF p-tau181, an indicator of tau burden, and (3) morphometry metrics (volume, cortical thickness) indicative of neurodegeneration.
Date: January 11, 2023, at noon
Location: on-site and via Microsoft Teams
Assistant Professor
Tech4Health Institute
Department of Radiology
NYU Grossman School of Medicine
The research goal of Dr. Cai’s lab is to build new paradigms of nanotechnology for biological and biomedical applications. At the Tech4Health Institute, his team is developing novel miniaturized optical and mechanical probes to measure and manipulate biological input and output signals. By borrowing nanolithographic technology from the semiconductor industry, his lab creates optical metasurfaces, planar optics, and cell nano-interfaces with unprecedented precision and functionality. Integrating these nanoengineered 2D surfaces on a series of functional platforms (e.g., 3D structures, soft materials, active micro/nano systems), innovations for broad applications become feasible in a dynamic and tunable fashion. In this talk, Dr. Cai will present his recent work on (1) Optical metasurfaces and nanophotonics for bioimaging and biosensing; (2) Nanoengineered cell interfaces for mechanobiology study. In particular, he will highlight the adoption of dielectric materials in optical metasurfaces and cell interfaces. Based on Mie resonance instead of plasmonics, dielectric metasurfaces eliminate the ohmic loss, thermal effects and interactions with fluorescence, which not only provide better optical efficiency, repeatability, stability, and bio-compatibility for imaging and sensing, but also enable super-resolution fluorescence microscopy for nanopattern-based cell mechanobiology study.
Date: December 15, 2022, at noon
Location: on-site and via Microsoft Teams
PhD Candidate
Electrical and Computer Engineering
University of Arizona
Deep learning (DL) models trained on large, labeled datasets are currently the state-of-the-art in several classification and segmentation tasks in medical imaging applications. However, obtaining expert manual annotations for data hungry DL models is time-consuming. Additionally, the performance of supervised DL models can be limited by the choice of data representation. This talk explores techniques for transforming images to their new representations that can improve the performance of DL models. The first work presents MR contrast synthesis as a mechanism to transform images to a better representation by utilizing domain knowledge regarding the task of interest. Motivated by the abundance of unlabeled MR imaging datasets compared to labeled ones, the next work explores representational learning techniques to provide suitable initialization for DL models for subsequent tasks. The improvements in segmentation performance from a novel contrastive learning approach with representational constraints derived from multi-contrast MR images is investigated.
Date: December 14, 2022, at noon
Location: on-site and via Microsoft Teams
Director of Radiochemistry
Department of Radiology
NYU Langone Health
Positron emission tomography (PET) is a diagnostic tool which utilizes a radiopharmaceutical (compound tagged with a positron emitter) and its detection of the emission of ɣ-rays (511 keV) that results from the annihilation of those positrons. It is an ever-growing field with newly-developed PET radiotracers being approved for clinical use and novel radiopharmaceuticals being reported in the literature. PET is a multi-field tool in which researchers in the areas of oncology, radiology, and psychology are able to benefit from its precise, real-time analysis. The aim of this talk is to give insight into our radiochemistry lab and to showcase what is possible with the use of PET radiotracers.
Date: December 7, 2022, at noon
Location: on-site and via Microsoft Teams
Instructor, Radiology
Stanford University
With the average age of the world population increasing very rapidly, diseases that restrict mobility such as osteoarthritis and sarcopenia are on the rise. Conventional imaging methods, including MRI, fail to detect early changes in cartilage and skeletal muscle caused by these diseases, resulting in a missed opportunity for timely treatments. In this talk, I will present several MRI methods that, by providing insight into structure and function, can better characterize healthy joints and skeletal muscle, as well as detect early signs of disease.
Osteoarthritis is not only a disease of cartilage, but it involves the entire joint. In particular, the synovium plays a fundamental role in the progression of osteoarthritis. In my talk, I will illustrate how quantitative MRI can facilitate the diagnosis of synovial inflammation in the knee, and how quantitative MRI can detect the effect of non-surgical treatments on articular cartilage.
Muscle aging is characterized by many compositional and structural modifications, that strongly affect muscle strength, and can lead to sarcopenia. In the second part of the talk, I will present technical advances and applications of several MRI methods, including T2-relaxometry, phase contrast, and diffusion MRI, that can provide insight into skeletal muscle composition and structure, as well as their connection to function.
Date: November 30, 2022, at noon
Location: on-site and via Microsoft Teams
Research Scientist
NYU Grossman School of Medicine
How we think about and study the brain—both in health and disease—is enhanced by our ability to see inside a living human person. And through the power of MRI, ‘seeing’ comes in the form of detailed, multi-contrast images with exquisite sensitivities to brain microstructure. Alterations in microstructure, such as changes in myelination or neuronal loss, can culminate in tissue atrophy and disruptions in neuronal connectivity that are detectable using in vivo neuroimaging. It is critical to detect such processes at the microstructural scale because, by the time damage becomes macrostructural, it is largely irreversible. While many quantitative imaging techniques have shown promise in characterizing microstructure, it remains a challenge to link specific cellular-level features to a single contrast or model parameter. In this talk, I will present a hypothesis-driven approach to pediatric imaging that integrates both prior knowledge from neurobiology with high-dimensional data sets to extract biologically relevant features from in vivo MRI. In particular, this talk presents the differences in microstructural hypoconnectivity in brain white matter of typically-developing children and children with a rare genetic disorder that impacts axonal morphology. I will then present a jointly assembled framework that fuses multimodal data across spatial scales within a translatable non-human-primate model (microscopy, ex vivo MRI, and in vivo MRI) to better inform our work on the impact of prenatal exposure to infectious disease. In summary, I will discuss the detection of neurobiological features with specificity through multimodal data sets with the goal of refining and possibly validating current standards of quantitative imaging.
Date: November 18, 2022, at noon
Location: 660 1ST AVE FL 3 and via Zoom
Graduate Student
BAOBAB unit
NeuroSpin
CEA Paris-Saclay, France
When programming MRI pulse sequences on a Siemens system, researchers may find it difficult to have access to sequence resources, including state of the art sequences and basic codes that are protected by intellectual property rights. Furthermore, they may have issues sharing sequence codes or getting inside the Siemens programming environment. In this context, we propose an open-source toolbox, developed in the IDEA toolkit, that provides a modular structure for MRI pulse sequence developments. With its object-oriented structure and its clear variable names, it allows to develop sequences such as spin echo or gradient echo, 2D or 3D, using single line or echo-planar reading. Also, it proposes some preparation tools in order to be able to program diffusion-weighted sequences using trapezoid or arbitrary-waveform diffusion encoding gradients, combined with a fat saturation pulse. GinkgoSequences has been made compatible with the Gadgetron1 tool for reconstruction and has already been used to program diffusion-weighted spin echo and STEAM echo-planar imaging sequences, which have successfully been tested on phantoms and on human volunteers.
REFERENCES
Date: November 11, 2022, at noon
Location: on-site and via Microsoft Teams
Founding Director, Center for Scientific Computation in Imaging (CSCI) at UC San Diego
Associate Director of Biomedical Applications, UCSD Center for Functional MRI
As scanner technologies continue to advance, the acquisition of high-resolution multivariate data increasingly facilitates more quantitative measures of physical systems. However, these more complex data require increasingly advanced computational methods for analysis and visualization. This applies to standard neuroimaging techniques such as MRI and EEG that are ubiquitous in both basic science research and in clinical settings as well as to other fields such as meteorology where imaging is performed by Doppler radar. Despite the ubiquity and importance of these methods, there remain significant challenges in extracting quantitative information from these data. In this talk, I will discuss some recent work on new theoretical and computational methods for 1) The analysis of multimodal MRI data, including structure from high-resolution anatomical MRI, local physiology and structural connectivity from diffusion MRI, and detection of functional brain networks from functional MRI; 2) Decoding and spatially resolving the EEG signal. Time and interest permitting, I will give a brief overview of meteorological applications of our methods.
Date: September 28, 2022, at 2:00 p.m.
Location: on-site and via Microsoft Teams
Associate Professor of Radiology
Biomedical Engineering and Imaging Institute (BMEII) at the Icahn School of Medicine at Mount Sinai
In this talk, I will present an overview of ongoing projects in my lab to extend the GRASP (Golden-angle RAdial Sparse Parallel) MRI framework for rapid continuous imaging. In the first part, I will describe a technique for 4D GRASP MRI at sub-second temporal resolution, which can be applied for imaging motion (or motion plus contrast changes) in real time. I will also describe the extension of this imaging method to a new framework, called MR motion fingerprinting, towards volumetric imaging with sub-second imaging latency that will be required in MRI-guided radiotherapy or interventions. In the second part, I will describe magnetization-prepared GRASP MRI (MP-GRASP), a framework for rapid motion-robust quantitative imaging and multiparametric imaging. Specific examples of MP-GRASP will include GraspT1, GraspT1-Dixon, GraspCEST, etc. Implementation of these techniques using deep learning (e.g., DeepGraspT1) will also be described. In the third part, I will briefly summarize our recent effort to extend GRASP-MRI to other sampling trajectories and applications, including golden-angle Cartesian (for imaging non-moving organs), golden-angle spiral (for imaging the lung), and golden-angle rotated PROPELLER/BLADE/EPI (for diffusion imaging).
Date: September 20, 2022, at noon
Location: on-site and via Microsoft Teams
PhD Candidate
Medical University Vienna, Vienna, Austria
MR Physics & Instrumentation Group, MGH Martinos Center, Charlestown, MA
In this talk I will discuss the rationale for thinking about high channel count arrays, the challenges in building them, and potential future directions.
Date: September 8, 2022, at 4:00 p.m.
Location: on-site and via Webex
Chair of Biomedical Engineering
Lam Woo Professor of Biomedical Engineering
University of Hong Kong
“Magnetic resonance imaging is a key diagnostic tool in modern healthcare, yet it can be cost-prohibitive given the high installation, maintenance and operation costs of the machinery. There are approximately seven scanners per million inhabitants and over 90% are concentrated in high-income countries. We describe an ultra-low-field brain MRI scanner that operates using a standard AC power outlet and is low cost to build. Using a permanent 0.055 Tesla Samarium-cobalt magnet and deep learning for cancellation of electromagnetic interference, it requires neither magnetic nor radiofrequency shielding cages. The scanner is compact, mobile, and acoustically quiet during scanning. We implement four standard clinical neuroimaging protocols (T1- and T2-weighted, fluid-attenuated inversion recovery like, and diffusion-weighted imaging) on this system, and demonstrate preliminary feasibility in diagnosing brain tumor and stroke. Such technology has the potential to meet clinical needs at point of care or in low and middle income countries.”
Abstract quoted from
A low-cost and shielding-free ultra-low-field brain MRI scanner”
Nature Communications
doi: 10.1038/s41467-021-27317-1.
Date: September 7, 2022, at 11:00 a.m.
Location: 660 1ST AVE FL 3 and via Webex
Professor of Radiology
University of Basel, Switzerland
No abstract was provided for this talk.
Date: August 2, 2022, at noon
Location: via Webex
Siemens Healthcare
This talk will provide an overview of Gregor’s research experience in MRI at Siemens Healthcare, including efforts to make an MR fingerprinting implementation robust and ready for clinical research, and explorations of further capabilities of the method. The talk will also explore recent developments in deep learning image reconstruction methods for turbo spin echo sequences and concepts for aiding radiologists by automatically detecting and diagnosing pathologies in MR images in the musculoskeletal domain.
Date: July 26, 2022, at noon
Location: on-site and via Microsoft Teams
Max Planck Fellow
Department High-field Magnetic Resonance
Director, Department of Biomedical Magnetic Resonance
University of Tübingen
I will talk about a new concept to accelerate parallel imaging by using dynamic instead of static receive coil sensitivities. This method is described by Felix Glang et al. in a recent paper titled “Accelerated MRI at 9.4 T with electronically modulated time-varying receive sensitivities”, published in Magnetic Resonance in Medicine. I will also show some novel concepts of how this approach can be extended to dynamic receive dipoles.
Date: July 6, 2022, at 11:30 am
Location: via Webex
Graduate Student
Cardiff University Brain Research Imaging Centre
High-resolution MR imaging is important for the qualitative and quantitative analysis of brain structures. Unfortunately, adverse subject motion during the acquisition introduces image blurring, lowers the image quality, and reduces the effective spatial resolution. The acquisition of fat-navigators has enabled the correction of motion-induced blur. Alongside developments in data acquisition, camera-guided 3D tracking to enable markerless motion correction has been commercialized. In this study, we aimed to investigate the impact of different types of head motion on brain MR images and compare the retrospective motion correction using fat navigators and markerless tracking devices.
Date: June 28, 2022, at noon
Location: on-site and via Zoom
Professor
Hadassah Medical Center
Hebrew University of Jerusalem
Alterations in the default mode network (DMN) are known to be associated with aging and with neurological and psychiatric diseases. We assessed age-dependent changes in interactions within and between the DMN and other brain areas and correlations of these interactions with a battery of neuropsychological tests to formulate a macroscopic model of aging.
Using a novel multivariate analysis method on resting-state functional MRI data from 50 young and 31 old healthy individuals, we identified directed intra- and inter-DMN pathways that differed between the groups and used correlations with neuropsychological tests to infer behavioral meaning.
We observed that visual and limbic inter-DMN pathways in old subjects engaged at low frequency, involved the dorsal posterior cingulate cortex (PCC), and correlated with reduced attention and working memory. In contrast, in young subjects, they were at high frequency and involved the ventral PCC. Sensorimotor-DMN pathways were efferent in young subjects and afferent in old subjects, with the latter correlated with reduced attention and working memory.
We suggest a macroscopic model of aging centered in the DMN. The model implies that the reduced sensorimotor efferent brought about from reduced physical activity and the increased need to control such activities by the medial prefrontal cortex (mPFC) causes a higher dependency on external than on internal cues. This results in a shift from ventral to dorsal PCC of inter-DMN pathways. Consequently, one way to slow these processes would be by increasing sensorimotor activity, therefore stressing the critical importance of physical activity and suggesting how it might slow aging.
Date: June 7, 2022, at noon
Location: via Webex
Graduate Student
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is clinically characterized by impaired cognitive function and memory loss. While plasma, cerebrospinal fluid, and PET imaging can provide biomarkers of the following AD pathological hallmarks: extracellular beta-amyloid (Aß) deposits (“A”), intracellular tau protein tangles (“T”), and atrophy due to synaptic and neuronal loss [“(N)”], pathophysiological processes at the earliest stages of the AD continuum are still largely unknown. Furthermore, AD is recognized as a heterogeneous disorder, whereby patients who fit the clinical criteria for AD may differ in levels of AT(N). To this end, proton magnetic resonance spectroscopy (1H-MRS) can shed light on neurochemical changes that may precede AT(N), and yield complementary biomarkers of AD pathology. In this talk, I will present preliminary findings from a 1H MRS study in cognitively normal subjects. Using MRSI EPSI for whole-brain coverage, we measured metabolite values from cortical gray matter regions implicated in the tau Braak pathway (which has a specific spatio-temporal spread): posterior cingulate (Braak IV), precuneus (Braak V), cuneus (Braak V); and a negative control region, the lateral occipital gyrus. Using sLASER for localization and improved quantification in the temporal lobe, we measured metabolite values from the left hippocampus (Braak II). For all metabolites in all regions, I examined (1) correlations between metabolite levels and atrophy, using morphometry metrics from structural MRI, (2) correlations between metabolite levels and CSF p-tau181, and (3) metabolite differences between APOE4 carriers and non-carriers.
Anna Chen is a third-year graduate student in Vilcek Institute’s biomedical imaging and technology PhD training program. Anna is advised by Ivan Kirov, PhD. She has a background in cognitive neuroscience, and is interested in using MRS techniques to better understand brain metabolism in disease.
Date: May 25, 2022, at noon
Location: via Webex
Assistant Professor
NYU Grossman School of Medicine
Accelerated MRI has made MRI exams faster and more affordable, making it possible to investigate new diseases and physiological processes in the human body. Artificial intelligence and machine learning tools are now common tools for image reconstruction and analysis. However, just recently these tools have been used to learn how to improve the MRI acquisition.
In this talk, we briefly review the evolution of undersampled acquisitions and the new machine learning tools for this task (such as LOUPE, BJORK, and), emphasizing the new tools that have been developed at NYU to improve the MRI acquisition for accelerated MRI. Our recently developed Bias-Accelerated Subset Selection (BASS) algorithm has improved the learning speed of the sampling pattern in compressed sensing and deep learning image reconstruction, allowing us to push even further the limits of acceleration in MRI.
Date: May 24, 2022, at noon
Location: via Webex
Postdoctoral Fellow
NYU Langone Health
Biophysical modeling of the diffusion MRI signal offers the exciting potential of bridging the gap between the macroscopic MRI resolution and the cellular level tissue microstructure, effectively turning our MRI scanner into a noninvasive in vivo microscope. In the brain white matter (WM), the standard model (SM) of the diffusion signal was proposed as a general framework unifying many previous WM models. However, careful histological validation is required. Previous efforts used histology from several modalities of microscopy to quantify tissue metrics, and used them to evaluate parameters obtained from diffusion MRI. Yet, a comprehensive histological validation of the SM so far has been lacking.We used segmented 3D electron microscopy and ex-vivo diffusion MRI to characterize sham and injured rat brain white matter microstructure and perform a comprehensive histological validation of the sensitivity and specificity of the SM parameters. The large number of segmented 3D axons, in the order of ten thousand per sample, allowed us to better quantify tissue properties compared with previous studies.
Date: May 3, 2022, at noon
Location: via Webex
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
The first near-infrared (NIR I) wavelength range of 650-950 nm is preferable in many optical biological imaging techniques due to reduced light absorption by hemoglobin and water. I will present in vivo functional neuroimaging findings from optical and optoacoustic imaging in the NIR I range. My work utilizes novel dyes and genetically encoded calcium indicators as well as endogenous hemodynamic signals.
Sarah is a doctoral candidate in the laboratory of Shy Shoham, PhD, at NYU Langone’s Tech4Health Institute. Her research is currently focused on functional neuroimaging with optoacoustic tomography.
Date: April 26, 2022, at noon
Location: via Webex
Graduate Student
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Recognition is a fundamental cognitive function during which the brain projects perceptual templates learnt from past experiences onto the current sensory input. A key factor in this process is how much the brain weights prior knowledge versus the sensory input. This weighting is known to vary between individuals and may contribute to hallucinations in mental illnesses when prior knowledge is weighted to a pathologically high degree. By manipulating human subjects’ ability to use prior knowledge to recognize stimuli during fMRI and electrocorticography neural recording, this project examines the underlying neural mechanisms of prior knowledge deployment across sensory modalities and distinct sources of prior knowledge. Specifically, this project examines visual and auditory recognition, and prior knowledge derived from lifelong learning as well as one-shot learning. This talk will present initial results and plans to integrate data from multiple experiments.
Jonathan Shor is a fourth-year graduate student in the Vilcek Institute’s Biomedical Imaging and Technology PhD Training Program working with Dr. Biyu He. Shor has a background in computer science and is interested in developing computational models of cognitive functions, such as conscious perception. His current focus in the Perception and Brain Dynamics Lab is establishing the neural mechanisms driving prior knowledge deployment during recognition.
Date: April 21, 2022, at noon
Location: via Webex
Master in Neuroscience Candidate
Paris Est-Creteil University
France
White matter bundles underline structural connectivity in the human brain: they link functional cortical and subcortical gray matter areas and thus, they allow complex interactions between these regions. Awake brain surgery is a unique opportunity to study these white matter fibers, to which we have physical access inside the post-resection cavity. In the present protocol, we study the structural connectivity using diffusion MRI – tractography and the effective connectivity using electrocorticography – subcortico-cortical evoked potentials. We attempt to correlate biomarkers provided by these two methods in order to better understand the propagation of electrical activity in the brain.
Petru Isan is currently undertaking a Master’s degree in Neurosciences at the Paris Est-Creteil University. He has graduated from the Tours Faculty of Medicine and is a second-year Neurosurgery resident at the Pasteur 2 University Hospital in Nice, France. At the moment, Petru is doing an internship at INRIA Sophia-Antipolis, where he is investigating how different brain regions communicate with each other.
Date: April 19, 2022, at noon
Location: via Webex
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Digital breast tomosynthesis (DBT) is a new imaging technique in mammography. Even though it is proven to be even more accurate than full-field digital mammography, false-positive recalls are still a subject of concern in the breast cancer screening setting. We developed an AI system using the screening mammography exams at NYU Langone Health which could save 29% of unnecessary recalls and potentially reduce radiologist workload by 40% while missing no malignancies. Specifically, our system consists of deep neural networks trained on both breast-wise pathology labels and a limited amount of pixel-level segmentation labels. The system can also highlight the location of suspicious findings on 2D and 3D mammography images for AI decision support.
Date: April 12, 2022, at noon
Location: via Webex
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Biophysical models provide specificity to the tissue microstructure with diffusion MRI. In the brain, the Standard Model (SM) of diffusion in white matter (WM) was proposed as an overarching framework unifying many previous WM models. To stabilize the parameter estimation in clinical datasets with limited information, different constraints have been adopted to the WM models, resulting in different outcomes. Meanwhile, a machine learning (ML) model has shown promise in estimating SM parameters without constraints. To evaluate these WM models, we first compare the accuracy and specificity of them in simulation. Then we apply these models to early brain development, multiple sclerosis, and stroke. Through this extensive comparison both in simulation and in several pathologies or processes, our goal is to determine the most reliable WM model for clinical datasets to extract tissue microstructure parameters.
Ying Liao is a doctoral candidate in Vilcek Institute’s Biomedical Imaging and Technology training program working with Els Fieremans, PhD, and Dmitry Novikov, PhD. He has a background in biomedical engineering and is interested in developing and employing machine learning (ML) methods to characterize tissue microstructure. His focus in the MRI biophysics lab is the ML-based estimation of white-matter parameters in the standard model of diffusion MRI.
Date: March 30, 2022, at noon
Location: via Webex
Research Assistant Professor of Radiology
Perelman School of Medicine
University of Pennsylvania
Structural neuroimaging is central to MRI’s role in both clinical practice and neuroscience. In addition to its role as a diagnostic modality, structural MRI provides the basis for cortical morphometric analyses that are widely used to study both developmental and degenerative processes. However, many populations of interest are often unable to remain still enough to produce the high-quality structural MRI needed for either clinical interpretation or quantitative morphometry. Moreover, while group differences in MRI-based morphometry have been presented across a variety of populations, it is generally difficult to extend these results to single-subject diagnoses.
I will present my ongoing work towards making structural neuroimaging methods both more robust, and more sensitive to disease processes. First, I will show how we have improved morphometric accuracy by studying motion as a source of bias and developing methods to help ameliorate these errors. As an example of this work, I will present refinements to our motion correction system for application in the upcoming HEALthy Brain and Child Development study. Second, I will show how we are developing intra-cortical measures by studying Frontotemporal Lobar Degeneration (FTLD) using joint ex vivo MRI and histopathology. I will present our recent findings of iron-rich pathology within the cortical laminae in FTLD, and discuss our plans for translating these results to in vivo imaging studies with the goal of single-subject diagnosis.
Date: March 29, 2022, at noon
Location: via Webex
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Aging is a major risk factor of neuronal loss and cognitive impairment (e.g., Alzheimer’s disease). Neurovascular abnormalities and brain atrophy are proved to be pathophysiological biomarkers in normal- and abnormal-aging brains. In vivo detection of microvascular changes underlying neurodegeneration plays a crucial role in early diagnosis and better understanding disease mechanism in age-related dementia. Susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) are sensitive to the deoxygenated hemoglobin in the veins, which can be used to map the venous vasculature and characterize venous (de)oxygenation level in the brain. This presentation will demonstrate two studies that use high resolution SWI and QSM 1) to characterize the venous oxygenation/utilization changes related to neurodegeneration in the elderly; 2) to map the in vivo venous vasculature of hippocampus at 7T. These studies aim to gather more evidence on the role of SWI/QSM as an early imaging marker of age-related neurodegenerative diseases.
Date: March 22, 2022, at noon
Location: via Webex
Assistant Professor of Mechanical Engineering
Director of the Center for Neuromechanics
Stevens Institute of Technology
White matter changes are a frequent observation in the aging human brain and are considered a reliable indicator for cognitive impairment and long-term functional decline. On T2-weighted fluid attenuated inversion recovery magnetic resonance images, these lesions appear as white matter hyperintensities (WMH) and are commonly associated with vascular degeneration. From a physics perspective, however, the persistent (onset) locations of periventricular WMHs along the edges of the lateral ventricles suggest involvement of mechanical (over)loading of the ependymal cells forming the functional brain-fluid barrier. We use computational modeling to systematically explore the relationship between brain aging, white matter changes, and WMH formation. To that end, we build anatomically accurate brain models and predict the mechanical loading of periventricular tissues. We observe that maximum ependymal cell stretch consistently localizes in the anterior and posterior horns irrespective of ventricular volume or shape. More importantly, these locations coincide with periventricular WMH locations observed in our patient scans. From these results, we pose that further analysis of white matter pathology in the periventricular zone that includes a mechanics-driven deterioration model for the ventricular wall.
Date: March 15, 2022, at noon
Location: via Webex
Senior Staff Scientist
Siemens Healthineers
Next month NYU will be the 6th facility in the US to receive an FDA approved CT scanner with photon counting capabilities. Photon counting CT represents a new paradigm in CT scanning. Compared to conventional CT where the energies of the photons incident on the detector are reported as a total sum, photon counting CT measures the energies of individual photons (i.e., “counts” them). In this talk, I will describe the fundamental physics of photon counting CT, the implications of this approach to scanning, and its clinical benefits.
Date: March 8, 2022, at noon
Location: via Webex
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Iron is critical for healthy brain biochemistry and function. While deficient peripheral iron was found to increase psychiatric morbidity risk, in vivo examination of non-heme brain iron in psychotic spectrum disorders (PSD) are lacking. The current study employed quantitative MRI to examine iron content in several iron-rich subcortical structures in a young adult PSD group. Using a modified cross-relaxation imaging method, we fitted R1 and macromolecular proton fraction maps and estimated region-wise R2* values using a linear regression model. Our findings suggest that subcortical non-heme iron deficiencies play a role in PSD risk and symptomatology and may precede both structural and myelin alterations.
Date: February 22, 2022, at noon
Location: via Webex
Doctoral Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
DCE-MRI is a critical imaging method used in cancer diagnosis. However, it is limited by long reconstruction time, and has inherent trade-off between temporal and spatial resolution. While machine learning has been shown to improve MR reconstruction quality in several studies, these methods require ground truth image data for training. For DCE-MRI reconstruction, we do not have access to a simultaneously high temporal and spatial resolution ground truth image. In this talk, I’m going to introduce a novel pipeline to simulate realistic ground truth training images based on pharmacokinetic models and anatomical structure. We trained the machine learning model to reconstruct images by using the simulated k-space and ground truth images. The result shows, with different models, machine learning models could reconstruct images with high image quality in less time. This simulation pipeline is available online, suitable for future development and exploration.
Date: February 15, 2022, at noon
Location: via Webex
Postdoctoral Fellow
Center for Advanced Imaging Innovation and Research
NYU Grossman School of Medicine
Biophysical modeling of diffusion MRI data is appealing due to its potential to provide specificity to pathological processes. However, robust parameter estimation of the Standard Model (SM) of diffusion in white matter has been elusive due to intrinsic model degeneracies. Machine learning approaches improve parameter estimates but at low SNR these are determined by the training data. We develop a theory to analyze this behavior as function of SNR. Finally, we use these results to explore the design of optimal scanner-specific protocols to enable SM estimates in 15-minute acquisitions where we show reproducible results. Combining protocol optimization and robust parameter estimation may enable quantitative microstructure mapping in clinical settings.
Date: January 25, 2022, at noon
Location: via Webex
Postdoctoral Fellow
Center for Advanced Imaging Innovation and Research
NYU Grossman School of Medicine
Visualizations of white matter fibers are reconstructed in vivo from diffusion MRI through tractography. To this end, dedicated reconstruction techniques need to identify spatial orientations of fibers, typically by seeking maxima of orientation distribution functions (ODFs). However, commonly used methods often fail to reconstruct fibers crossing at shallow angles below 40 degrees. We aim to break this barrier with our proposed approach called ODF-fingerprinting (ODF-FP).
In this talk, I will introduce the concept of ODF-FP and the process of generation of ODF dictionaries that covers biologically plausible microstructure parameters. I will present the accuracy of crossing fibers reconstruction with ODF-FP in numerical simulations, diffusion phantoms, and the mouse model. In the latter, I will show the ODF-FP reconstruction of the optic pathways from in vivo diffusion MRI acquisition validated with the manganese chloride enhancement of the reconstructed tracts.
Date: January 11, 2022, at noon
Location: via Webex
Postdoctoral Fellow
Center for Advanced Imaging Innovation and Research
NYU Grossman School of Medicine
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a very high sensitivity in detecting breast cancer, but it often leads to unnecessary biopsies and patient workup. In this project, we developed and used an artificial intelligence (AI) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. In a clinical validation study, the AI system was found to be statistically equivalent to 5 board-certified breast radiologists. Radiologists’ performance improved when their predictions were averaged with AI’s predictions. We demonstrated the generalizability of the AI system using multiple data sets from Poland and the U.S. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. We showed that the AI system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding benign biopsies in up to 20 percent of all BI-RADS category 4 patients. This exploratory work creates a foundation for deployment and prospective analysis of AI-based models for breast MRI.
Date: December 21, 2021, at noon
Location: via Webex
Postdoctoral Fellow
Center for Advanced Imaging Innovation and Research
NYU Grossman School of Medicine
MRI technology is being continuously developed and new MRI scanners are installed every year. However, most of them are used clinically, without being available to researchers. Furthermore, the majority is in western countries. As a result, access to MRI research and training has been limited.
Several open-source software tools are available to simulate different aspects of the MRI experiment, from hardware design to signal encoding and image reconstruction. However, their dissemination has been limited because they are not general enough, often lack documentation, and require extensive background knowledge. Nevertheless, they have enormous potential.
The goal of this project is to integrate, generalize and extend this existing software to develop a comprehensive open-source software platform to simulate the complete lifecycle of an MRI experiment. By relying on a web-based graphic user interface and cloud computing, Cloud MR will enable anyone with an internet connection to perform MRI research anywhere in the world. Furthermore, it will be a unique tool for MRI training, which is increasingly needed due to the clinical widespread use of MRI.
Date: December 7, 2021, at noon
Location: via Webex
Research Director
Commissariat à l’Energie Atomique (CEA)
Paris, France
Basic understanding of brain metabolite diffusion as measured using diffusion-weighted magnetic resonance spectroscopy (DW-MRS) in vivo has progressed over the last years, allowing relevant interpretation of DW-MRS in terms of cellular microstructure. When combined with adequate modeling, DW-MRS may even allow cell-specific (neuron versus astrocytes) quantification of some microstructural parameters. We will see how recent results suggest that DW-MRS may actually open perspectives beyond cell structure determination, namely characterizing diffusion in the extracellular space, and assessing the distribution of brain lactate between neurons, astrocytes, and the extracellular space, which is a highly relevant neuroscience question related to energy metabolism and the astrocyte-to-neuron lactate shuttle.
Date: December 3, 2021, at noon
Location: via Webex
Professor and Scientific Director
Department of Diagnostic and Interventional Radiology
University Medical Center Freiburg, Germany
Spiral MRI has been known since 1983. Spiral trajectory offers an extremely fast and efficient way to cover two-dimensional k-space with an intrinsically one-dimensional trajectory – much more efficient than the line-by-line scanning of commonly used Cartesian sampling. Spirals have additional advantages like intrinsic motion compensation. They still haven’t made it into clinical routine due to their extreme sensitivity against deviations of the actual from the nominal trajectory and against off-resonance effects, where even slight inhomogeneities due to susceptibility effects lead to strong image artifacts.
The presentation will discuss principles and implementation of single shot spiral TSE at 1.5 and 3 T. High-quality images with 1 mm in-plane resolution are acquired in < 200 ms, allowing extremely fast screening, e.g., in non-cooperative patients.
Date: November 30, 2021, at noon
Location: via Webex
Postdoctoral Fellow
NYU Langone Health
Hybrid-state Free precession (HSFP) is a quantitative transient state technique that allows the rapid mapping of relaxation times, in part, due to the high signal which results from a fully balanced sequence design. However, this design makes the transient magnetization of HSFP highly susceptible to disruptions caused by eddy-current induced phases. These eddy current artifacts in balanced sequences result from large jumps in k-space. The 3D kooshball HSFP sequence samples the spin dynamics repeatedly while acquiring different parts of k-space. We swap individual k-space lines between different repetitions in order to minimize jumps within each repetition. This reordering can be formulated as a traveling salesman problem, and we tackle the discrete optimization with a simulated annealing algorithm.
In the second part, HSFP-based quantitative MRI is applied at 0.55 T. Here, the SNR-efficient design of HSFP allows quantitative maps to be obtained in 12 mins with 1 mm isotropic resolution. Further, a theoretical analysis of the Cramér–Rao bound is performed, and the expected SNR is compared to 3T.
Date: November 23, 2021, at noon
Location: via Webex
PhD Candidate
Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
The disruption of blood-brain barrier (BBB) is associated with various pathologies in the brain. Dynamic contrast-enhanced MRI has been widely used as a tool for quantitatively measuring the changes in the microstructural environment in brain. In recent years, there has been increasing interest in measuring the BBB disruption in Alzheimer’s disease (AD) and normal aging. However, unlike diseases that exhibit substantial changes in the brain, such as brain tumors, the BBB disruption in AD or aging is suggested to be very subtle.
We utilize the Golden-angle RAdial Sparse Parallel (GRASP) sequence to effectively measure this subtle disruption and to overcome the following three challenges: (a) reducing the long scan time needed to observe small extravasation of the contrast agent, (b) obtaining the arterial input function with the help of AI, and (c) validating and measuring the sensitivity of the current approach at different levels of BBB disruption.
To address these challenges, we have (a) developed a novel pharmacokinetic model suitable for measuring BBB disruption with reduced scan time, (b) trained and implemented a deep neural network to deterministically estimate the capillary-level input function, and (c) conducted an animal study to artificially induce different levels of BBB disruption, comparing the sensitivity of measuring subtle disruption via the conventional Gadolinium contrast agent exchange rate and the water exchange rate using the Ferumoxytol contrast agent.
Date: October 26, 2021, at noon
Location: via Webex
Assistant Professor
Center of Intelligent Imaging (CI2)
Department of Radiology and Biomedical Imaging
University of California, San Francisco
Active multi-disciplinary research is ongoing to discover quantitative biomarkers for early diagnosis, monitoring, and assessment of joint degeneration. Medical imaging has played a substantial role in this area; for example, radiographs can detect structural alterations in bone, but these scans have low sensitivity for detecting tissues that are thought to be important in joint degeneration in osteoarthritis (OA) (such as cartilage, menisci, and other soft tissues) and cannot capture changes in bone marrow (i.e., bone marrow lesions). Conversely, magnetic resonance imaging (MRI) has higher sensitivity than radiography in detecting soft tissue changes, bone marrow edema, and early osteophytic changes. Advanced quantitative imaging techniques, novel computerized image post-processing, and more recent machine learning (ML) techniques have made possible further advances towards quantitative characterization of early joint degeneration and identification of imaging biomarkers associated with OA. Deep learning advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times and processing of MRI, conducting large-scale longitudinal studies, and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field. Compared with labor-intensive human efforts, DL-based methods have advantages and potential in all stages of imaging as well as post-processing steps, including aiding in diagnosis and prognosis. In this talk, I’ll explore how recent applications of DL have improved imaging-based understanding of knee OA. We illustrate how DL techniques are applied at all stages of imaging to enable automation of acquisition analysis and new imaging biomarkers discovery.
Date: October 19, 2021, at 2:00 p.m.
Location: via Webex
Research Scholar
Memorial Sloan Kettering Cancer Center
Diffusion weighted imaging is a powerful technique sensitive to tissue microstructure. Three possible applications of this technique will be presented: (a) to early detect slow growing diffuse glioma models; (b) to evaluate the effect of ageing on mouse muscle microstructure in dystrophic and healthy mice; (c) to image the motor unit activity in the human muscles.
a) Diffuse gliomas (WHO grade II to IV) are the most common primary brain tumours in humans. Their diffuse infiltration into the surrounding normal brain precludes complete resection and they all eventually recur, usually having progressed to a more aggressive tumour. The infiltrative part, which is “invisible” using conventional T1 and T2-weighted MRI is difficult to target with treatment. We investigated whether diffusion MRI might be a useful method to detect the microstructural changes induced in the normal brain by the slow infiltration of glioma sphere cells. Localized proton MR spectroscopy of lesions and immunohistochemical assessment were compared with imaging results.
(b) During postnatal development, muscle fibres grow enormously and the sarcolemma dynamically and constantly expands. Investigating this process in the time up to maturity may help the understanding of clinical onset of infant myopathies, such as Duchenne muscular dystrophy.The evolution of hindlimb muscle microstructure between young (development) and adult mice was investigated in dystrophic and healthy muscles using diffusion-weighted imaging protocols and histology. Multiple diffusion times (range: 25 – 350 ms) were explored, and significant differences between the diffusion properties of hindlimb muscles in healthy and diseased mice were found for long diffusion times, with increased water diffusivity in dystrophic mice. Muscle fibre size distributions showed higher variability and lower mean fibre size in dystrophic than wild-type animals. The extensive uptake of Evans Blue Dye in dystrophic muscles revealed a substantial sarcolemmal damage, suggesting diffusion measurement as more consistent with altered permeability rather than changes in muscle fibres.
(c) Neuromuscular diseases can lead to characteristic changes in the anatomy of motor units (MU). A diffusion-weighted imaging protocol sensitive to contraction was developed by synchronizing in scanner-electrical stimulation of MU with a pulsed gradient spin-echo imaging sequence. The spatial pattern of muscle fibres forming different MU was imaged in six healthy controls and subsequently in four patients with confirmed amyotrophic lateral sclerosis (ALS). Florid fasciculation in ALS patients was revealed at a frequency higher than healthy controls.
Date: October 12, 2021, at noon
Location: via Webex
Postdoctoral Fellow
Department of Radiology
NYU Langone Health
The single frequency nature of magnetic resonance (MR) allows the design and optimization of fast and robust algorithms for computational electromagnetics, based on integral equations. Specifically, surface integral equations (SIE) can be employed to analyze radio-frequency (RF) transmit-receive coils, while volume integral equations (VIE) can model the interactions between RF fields and biological tissues with finite electrical properties. The fast and accurate estimation of the above interactions is critical for optimal RF coil design at ultra-high-field MRI and to solve inverse problems based on MR measurements. For example, rapid SIE/VIE methods can be used to estimate tissue electrical properties, as in Global Maxwell Tomography, or to estimate coil sensitivities, as in Maxwell Parallel Imaging. This talk will describe our latest developments in these areas and present results for various applications of computational electromagnetics at ultrahigh field MRI.
Date: October 6, 2021, at 2:00 p.m.
Location: via Webex
Assistant Professor of Biomedical Engineering
Assistant Professor of Radiology
Northwestern University
Chicago, IL
More than 12 million Americans currently carry a form of orthopedic, cardiovascular, or neuromodulation device, and the number grows by 100,000 annually, driving the medical implant market to reach $160 billion by 2022. It is estimated that 50%-75% of patients with active electronic implants will need to undergo MRI during their lifetime, with some needing repeated examinations. Recent advances in device engineering have led to a new generation of electronic implants that are largely immune to MRI-generated static and gradient fields. However, tissue heating from radiofrequency (RF) excitation fields remains a major issue.
This talk will give an overview of recent advances in the development of specialized MRI hardware for implant-friendly imaging, with a focus on patients with deep brain stimulation implants. We will then discuss the unique role of computer modeling in MRI safety assessment, recent success stories in guiding surgical practices toward MR-friendly device implantation, and novel insights into RF heating of implants in new MRI platforms (e.g., vertical scanners). Finally, we will talk about the feasibility of applying machine learning for real-time risk assessment of MRI in patients with conductive implants.
Date: October 1, 2021, at noon
Location: via Webex
Professor
Moore Sloan Faculty Fellow/ Assistant Professor
Center for Data Science
New York University
There exists an abundance of neural network techniques that achieve impressive performance on various visual processing tasks. Most of these methods require full supervision and large annotated training datasets. Collecting such training datasets and annotations is often expensive and time-consuming, or not possible due to limitations on data privacy. In this talk, I discuss how weakly supervised learning and synthetic data generation can be used as a substitute when full training data annotations are not available. The proposed methodologies are evaluated on various computer vision and medical imaging benchmarks.
Date: September 28, 2021, at noon
Location: via Webex
Professor
Montford G. Cook Endowed Chair of Bioengineering and Electrical Engineering and Computer Sciences
UC Berkeley
Magnetic Particle Imaging (MPI) is an emerging noninvasive biomedical imaging modality that shows great promise for vascular and cellular imaging. MPI uses different physics from all conventional imaging modalities. MPI offers ideal “positive” tracer contrast, because human tissues produce zero MPI signal and tissue is magnetically transparent. The signal comes only from superparamagnetic iron oxide tracers (25-nm SPIOs). MPI has very high molar sensitivity because the SPIO magnetization is 51 million times stronger than the nuclear magnetization, M0, imaged in a 3.0T MRI. Indeed, we can even detect 1 micromolar [Fe] concentrations in seconds with quantitative, linear and positive contrast. MPI technology has not reached its true physics limit; we believe MPI could soon achieve single-cell sensitivity and 100-micron resolution with optimized tracers.
MPI applications today already compete with nuclear medicine studies on dose-limited sensitivity. But MPI offers a zero-radiation option for: tracking, cellular therapies; pulmonary embolism detection with 3D ventilation-perfusion MPI, akin to Tc99m V/Q studies; capillary-level noninvasive CBV & CBF for stroke or traumatic brain injury, or to rule out a traumatic gut bleed (akin to RBC-Tc99m scintigraphy). Cancer MPI is more challenging but soon could provide a noninvasive screening alternative to X-ray mammography for high-risk women with radiologically dense breast tissue. We recently showed that antibody labeled SPIOs can track WBCs (akin to In-111 WBC studies). WBC-MPI could emerge to be the best method to image infection, inflammation or cancer, and a powerful tool for optimizing immunotherapies. An important advantage relative to scintigraphy GI bleed and V/Q studies (which can take up to 3 hours including prep and scan) is speed: the targeted magnetic tracers can be safely injected immediately from the refrigerator providing first scans in just a few minutes. A crucial advantage of WBC-MPI is zero radioactivity of the tracers. CAR-T and CAR-NK cell therapies cannot survive the radiation dose of In-111 and so you cannot use standard nuclear medicine tools to track these exciting immunotherapies. Indeed, Immuno-MPI could soon become medicine’s most powerful tool for optimizing immunotherapies. Our lab has recently developed a potential breakthrough in MPI that already shows dramatic SNR and spatial resolution dramatically (10-fold for SNR and linear resolution). Experiments and physical models show that chained SPIOs act like ferromagnetic particles, with remanence and coercivity. This is well-modeled as a positive, regenerative feedback control system akin to Schmitt trigger comparators. Moreover, the new tracers show enormous improvements in SNR and spatial resolution, allowing for up to 1000-fold reduction in voxel volume. Our new tracers are not superparamagnetic (SPIO); they are actually superferromagnetic particles. We will show that superferromagnetic tracers could remove the final obstacle to human MPI, allowing for safe 1mm resolution in a human MPI scanner with cost comparable to a human whole-body 0.5T MRI scanner.
REFERENCES
Date: September 27, 2021, at noon
Location: via Webex
Regents Professor
Georgia State University
Atlanta, GA
Acute and chronic human diseases including liver and lung diseases, cancer, cardiovascular diseases and virus infection, share common key determinants including inflammation and fibrosis. In order to facilitate early detection, staging, and treatment responses, it is essential to develop a non-invasive imaging methodology that will allow us to longitudinally map and quantify the dynamic changes of inflammation biomarkers, such as chemokine receptors and collagen expression, during disease progression and upon treatment. Here we report our recent breakthrough in optimization, characterization, formulation, and production of a set of novel human protein-based contrast agents (ProCA®s) pioneered by our team for both preclinical and clinical applications.
We have developed a human collagen-targeted MRI contrast agent (hProCA32.collagen) with optimized binding fibrosis specificity. hProCA32.collagen exhibits 6.7-fold and 13.7-fold higher binding affinities for collagen type I over types III and IV, respectively. Our developed inflammation and fibrosis biomarker-targeted contrast agents specifically delineate activation of several types of cells and can capture the pro-metastasis niche and fibrosis associated with fatty liver and tumor microenvironment. With newly enabled dual and multi-color MR imaging methodology (precision imaging by MRI, pMRI) at multiscale, we have achieved robust longitudinal detection of early-stage liver and lung fibrosis, as well as micro-metastasis quantification of molecular biomarker changes for staging and monitoring treatment responses. We are moving rapidly toward clinical applications in early detection, monitoring progression, image-guided intervention/treatment, and patient stratification against human diseases including NASH, ASH, IPF, COPD, and metastasis from multiple cancers.
Acknowledgement: This work was supported in part by the NIH grants R01DK126080, R33CA235319, R42CA183376, R42AA025863, UT2AA028659, and S10OD027045 to Jenny Yang.
REFERENCES
Date: September 16, 2021, at noon
Location: via Webex
Clinical Research Collaborator
Breast Imaging Program
University of Cambridge, UK
Breast cancer is the most common cancer in the UK and in women globally. Imaging methods like mammography, ultrasound (US) and magnetic resonance imaging (MRI) play an important role in the diagnosis and management of breast cancer; they are generally utilized to provide anatomical or structural description of tumors in the clinical setting. It is widely accepted that the tumor microenvironment influences the phenotype, progression and treatment of breast cancer. This gave the impetus to move beyond tumor visualization in images to radiomics in order to provide additional disease characterization and early biomarkers of tumor response.
Due to their ability to assess physiological processes in vivo, positron emission tomography (PET) and MRI can provide non-invasive characterization of the tumor microenvironment, including perfusion, vascular permeability, cellularity and hypoxia, which is associated with poor clinical outcome and metastasis. Clinical imaging studies in breast tumors have hitherto assessed tumor physiological parameters separately, with only few directly comparing data from these modalities. To this end, hybrid PET-MRI represents an attractive option as it can allow examination of dynamic functional processes and features of tumors simultaneously, while also conferring methodological advantages to the way imaging information is combined.
The main aim of this work is to provide a better understanding of breast cancer pathophysiology using simultaneous PET and multi-parametric MRI. In particular, this work aims to explore relationships between imaging biomarkers of tumor vascularity measured by dynamic contrast-enhanced (DCE) MRI, cellularity using diffusion-weighted imaging (DWI) and hypoxic status using 18F-fluoromisonidazole (18F-FMISO) PET. Correlations between functional PET-MRI parameters and immunohistochemical (IHC) biomarkers of hypoxia and vascularity as well as MRI morphological tumor descriptors are also presented. This study concludes with an investigation of the utility of MRI markers of perfusion to quantitatively monitor and predict pathological response in patients undergoing neo-adjuvant chemotherapy (NACT) as surrogate markers of hypoxia.
Date: July 20, 2021, at noon
Location: via Webex
Associate Professor of Psychology
Director of the Neuroimaging Facility
New York University Abu Dhabi
Effective treatment of perceptual disorders such as amblyopia requires that we pinpoint the site(s) of neural impairment. Classic results indicate that the reduced visual acuity and contrast sensitivity in amblyopia are associated with smaller cell bodies in the LGN of the thalamus and a weaker drive of activity in cortex. However, it is unknown if the LGN is the first site of neural impairment, or if earlier retino-thalamic projections are affected as well.
We used diffusion MRI to quantify the white matter integrity of the retino-thalamic pathway in amblyopes and age-matched controls. We found reductions in fractional anisotropy in both the optic nerve and optic tract compartments of the retino-thalamic pathway of amblyopes. Moreover, when comparing between anisometropic and strabismic subtypes of amblyopia, we found that much of the effect was driven by anisometropic amblyopia.
These results suggest that the perceptual deficits that characterize amblyopia are due in part to impairments in the earliest segments of the brain’s visual processing pathway. Moreover, the treatment of the disorder may require different interventions and timecourses depending on the type of amblyopia. Future work should separately consider the impact of anisometropic and strabismic amblyopia, and carefully re-consider if the optic nerve impairments may be detected using retinal imaging methods.
Date: July 13, 2021, at 10:00 a.m.
Location: via Webex
PhD Researcher
Computer Science
University of Canterbury
Accurate segmentation of substantia nigra (SN) and red nucleus (RN) is challenging, yet important for understanding brain diseases like Parkinson’s disease (PD). This work proposes improved algorithms to segment SN and RN from T2*-weighted images and quantitative susceptibility mapping (QSM) MRI. After optimising segmentation algorithms to produce reliable SN and RN segmentations, multiple MRI (QSM, R2*, diffusion tensor imaging, arterial spin labelling, and volume) metrics extracted from the SN and RN are compared across groups (both PD/healthy controls and across cognitive subgroups) and investigate relationships with global cognitive ability and motor function in PD employing Bayesian regression models, and interesting evidence of associations is obtained. The multi-modal MRI features are also utilized to distinguish healthy controls and PD using a linear support vector machine (SVM) classifier. Therefore, multiple imaging modalities measuring complementary tissue characteristics such as iron deposition, microstructural alterations, perfusion changes, and volume atrophy may be useful for monitoring several ongoing processes in midbrain nuclei in Parkinson’s disease and also could be helpful for the distinction between PD and controls.
Date: July 6, 2021, at noon
Location: via Webex
Research Staff Scientist
CIBM MRI EPFL Section
There is currently no microscopy technique that qualifies as in vivo and non-invasive… or is there? It had been a long winding road, but diffusion MRI combined with biophysical modeling may just fill that role. In this talk, we will (i) go through the most recent validation steps of the white matter microstructure model and its applications to characterize white matter pathology, (ii) start from scratch to build a relevant model for cortical gray matter, and (iii) take a fresh look at diffusion functional MRI as a promising alternative to BOLD, particularly at low field strength.
Date: June 30, 2021, at noon
Location: via Webex
Computational Science and Engineering
Michigan Technological University
Light field (LF) photography has properties such as refocusing, perspective change, and occlusion removal that yield breakthroughs in microscopy, 3-dimensional (3D) displaying and rendering, as well as improvements in augmented and virtual reality. We can extract all of these properties by LF post-processing. However, a high-quality LF is bulky, making these post-processing computations time-consuming and challenging for real-time deployment. To reduce the wait time for LF data compression, we have merged conventional image compression techniques with residual convolutional neural networks for LF compression, making the task of LF storing and streaming more than two orders of magnitude faster than the state-of-the-art. Additionally, we developed RefNet to extract a set of refocused images from the raw LF in real-time as an example of the credibility of machine learning techniques for LF feature extraction. While our proposed RefNet is faster in estimating the refocused images than classical methods, it is more robust than current state-of-the-art non-learning methods in color prediction, where discretizing the image can cause artifacts.
Date: June 22, 2021, at noon
Location: via Webex
Graduate Student, Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
The act of recognizing a percept involves matching the sensory input with one’s prior knowledge to arrive at a best fit. When input is ambiguous, the best fit may be so poor that recognition fails. However, this threshold varies among individuals, up to pathological cases in which the weight given to prior knowledge is so great that individuals “recognize” things that are not there, as in the case of hallucinations often suffered by schizophrenia patients. Distinct sources of prior knowledge have been identified to influence recognition, including one-shot learning, lifelong learning, and expectation, but the neural mechanisms underlying this process are poorly understood. By using two paradigms that manipulate the deployment of prior knowledge while recording neural activity with 7T fMRI and electrocorticography (ECoG), I will test for a common neural process modulating the weight given to one-shot learning and lifelong prior knowledge in visual processing tasks. In this talk, I will present these paradigms along with some preliminary data I have collected, discuss further analysis plans, and possible follow-up studies.
Date: June 8, 2021, at noon
Location: via Webex
PhD Candidate
A.I. Virtanen Institute for Molecular Sciences
University of Eastern Finland
Three-dimensional electron microscopy (EM) techniques have enabled acquiring images of hundreds of micrometers of tissue with synaptic resolutions—images whose size can range from gigabytes to several petabytes. Applying manual or semi-automated methods for tracing and analyzing individual ultrastructures, even for a small section in such datasets, consumes hundreds of hours of experts’ time.
We developed ACSON and DeepACSON pipelines to automatically segment the entirety of neuronal processes in multi-resolution EM volumes of white matter. In ACSON, we automatically segmented white matter ultrastructures in high-resolution small field-of-view EM volumes. In DeepACSON, we emphasized low-resolution EM imaging to cover larger fields of view where severe membrane discontinuities became unavoidable. DeepACSON performed convolutional neural network (CNN)-based semantic segmentation and cylindrical shape decomposition (CSD)-based instance segmentation. CSD is a top-down instance segmentation algorithm we designed to decompose under-segmented myelinated axons into their constituent axons, accounting for the tubularity of axons as a global objective.
ACSON and DeepACSON segmented hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores, enabling comprehensive 3D morphometry of the white matter ultrastructures and capturing nanoscopic morphological alterations in healthy and pathological brains.
Date: May 25, 2021, at noon
Location: via Webex
PhD Candidate
Medical Engineering and Medical Physics
Harvard-MIT Division of Health Sciences and Technology
The sensitivity and specificity of brain MRI are limited by the low image encoding efficiency, leading to long acquisition time and limited spatial resolution especially for in vivo imaging. In order to address this, this talk will present our newly developed acquisition method, Echo Planar Time-resolved Imaging (EPTI), which uses novel encoding strategies in the high-dimensional space, together with efficient data sampling schemes, to allow better use of multi-channel receiver coil arrays and shared data correlation to achieve high acceleration capability.
EPTI has been extended and applied to improve the efficiency of quantitative relaxometry, functional and diffusion imaging. We demonstrate that the significantly improved imaging efficiency enables ultra-fast multi-parametric mapping at submillimeter isotropic resolution with an order-of-magnitude faster acquisition speed, functional MRI with higher neuronal specificity as well as dMRI with higher SNR efficiency and better structural integrity. The future application of the proposed techniques should improve the diagnosis power of clinical brain MRI and allow further understanding of the structural and functional organization of the human brain.
Date: May 11, 2021, at noon
Location: via Webex
Graduate Student, Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Intracortical myelin is a critical feature of the cortical mantle that is assumed to closely relate to high-order cognitive and behavioral functioning. Its abnormalities have also been implicated in a myriad of psychiatric and neurodegenerative disorders including schizophrenia and Alzheimer’s disease. One of the challenges in studying the cerebral cortex is the presence of non-uniformly distributed microstructural features across cortical layers. In this talk I’ll discuss how we may utilize myelin variations across the cortex and characterize cortical myeloarchitecture using cortical profiles sampled from high-resolution MRI images. Findings from applying this method in our schizophrenia dataset and the Human Connectome Project Aging dataset will be presented.
Date: April 28, 2021, at noon
Location: via Webex
Postdoctoral Fellow
UMC Utrecht
No abstract was provided for this talk.
Date: April 27, 2021, at noon
Location: via Webex
Graduate Student, Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Genetically encoded fluorescent calcium indicators are a crucial tool for preclinical neuroimaging. Most of these indicators have fluorescent excitation and emission ranges at visible wavelengths, with few reliable indicators existing in the biologically useful near-infrared range. In the past few years, some progress has been made on developing near-infrared indicators. I will present my ongoing in vivo work with NIR-GECO2G, a state-of-the-art near-infrared calcium indicator.
Date: April 16, 2021, at noon
Location: via Webex
Professor of Biomedical Engineering and Radiology
Chief, Division of Synthetic Biology and Regenerative Medicine, Institute for Quantitative Health Science and Engineering
Affiliated: Neuroscience program, department of Electrical and Computer Engineering, BEACON Center for the Study of Evolution in Action Michigan State University
The use of advanced imaging technologies has increased significantly in the past two decades and has revolutionized patients’ treatment on a daily basis, in terms of earlier and more accurate diagnosis. Essentially, no critical medical decisions are taken without relying on some sort of imaging. In the future, these decision-making processes will rely, to an even greater extent, on molecular imaging, in which personalized imaging probes, designed for specific medical conditions, will be used for diagnosis and to assess treatment success, by allowing clinicians to monitor therapy non-invasively and over time. Dr. Gilad's research is in the intersection of synthetic biology and molecular imaging, where his lab is implementing the principles of synthetic biology to develop cutting-edge technologies for better understanding the central nervous system and cancer. We bioengineer genetically encoded gene circuits and novel fusion proteins based on unique properties adopted from a variety of organisms. The Gilad lab has been focusing on bioengineering genetically encoded reporters for MRI mostly based on chemical exchange saturation transfer (CEST). Using protein engineering tools and machine learning algorithms, we have improved the sensitivity and expanded the arsenal of reporters. These reporters were implemented in an array of in vivo models with an emphasis on neuroimaging and cancer. We complement our reporters with genetically encoded optical sensors that allow detecting neurotransmitters.
Date: April 7, 2021, at 12:30 p.m.
Location: via Webex
Assistant Professor
Icahn School of Medicine at Mount Sinai
New York, NY
The GRASP project, started in 2011, is 10 years old today! GRASP MRI represents years of innovation and efforts by a research team consisting of MRI physicists, clinician scientists, and industry partners. To date, GRASP MRI has been successfully demonstrated in many clinical applications; its overall performance has been greatly improved after years of optimization, and it has also been extended to a number of new variants. In this talk, Li will take this opportunity to summarize the GRASP developments over the past decade and to discuss future directions that GRASP MRI could potentially be heading. Of course, in the era of artificial intelligence, how to make a smart version of GRASP by incorporating the latest deep learning technology is an important question we have to think and plan. If you are interested in hearing the latest of this project, you won’t want to miss this story.
Date: March 17, 2021, at 1:00 p.m.
Location: via Webex
Graduate Student, Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Traumatic brain injury (TBI) is a global health concern, with mild TBI (mTBI) accounting for 60%-80% of cases. TBI sequelae can be histologically explained by axonal varicosities known as diffuse axonal injury, but this pathology is not detectable using conventional CT and MRI. 1H MRS is a technique sensitive to neurochemical alterations which may enable more precise evaluation of TBI severity and prognostication when macroscopic structural damage is lacking. Unfortunately, varying results in regard to which metabolite(s) are most likely to be affected and what brain region(s) should be sampled, contribute to the limited clinical use of MRS in TBI. 1H MRSI has shed light on the regional distribution of metabolite findings, but a key part of translating the new knowledge to the clinic rests on determining how reproducible are the results of any particular study. This talk will present 1) initial data from a project intending to test the reproducibility of 1H MRSI findings from previous studies with a different mTBI cohort, 2) an outlook on future directions, as well as 3) recent findings from sodium imaging.
Date: March 17, 2021, at 1:00 p.m.
Location: via Webex
Postdoctoral Researcher
Previously at the Department of Cognitive Neuroscience, Maastricht University
Neurodegenerative diseases such as Alzheimer’s disease cause changes and disruption to cortical microstructure and architecture. MRI could potentially be sensitive to such changes. There is a growing interest in modelling human cortical areas using a combination of quantitative MRI and 3D microscopy ex vivo. This presentation contains a brief review of MR modalities that could be used for this purpose in addition to a Monte Carlo simulation study of DWI in light fluorescence microscopy samples.
Date: March 16, 2021, at noon
Location: via Webex
Doctoral Candidate, Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Our effort at NYU School of Medicine towards building deep neural networks for Digital Breast Tomosynthesis (DBT) volumes ranked #1 at the DBTex challenge. In this international challenge, we built an AI system to find biopsy-proven lesions from the DBT volumes collected from the Duke University Hospital. In this talk, I will discuss how our team was able to reach the best performance on the external dataset by utilizing our own private datasets at NYU Langone and how the model outputs could benefit the radiologists.
Date: March 3, 2021, at 2:00 p.m.
Location: via Webex
Chief, Division of Neuroengineering, Institute for Quantitative Health Science and Engineering
Michigan State University
East Lansing, MI
No abstract was provided for this talk.
Date: March 2, 2021, at noon
Location: via Webex
Doctoral Candidate, Biomedical Imaging and Technology
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Water exchange between compartments in the brain (e.g., the vascular, ventricular, extracellular, and intracellular spaces) is a crucial biological process for maintaining homeostasis and may serve as a biomarker for diagnosis of structural and functional deficits. FEXI and DKI(t) are promising diffusion MRI techniques for measuring apparent exchange in the brain. FEXI employs a double-diffusion-encoding scheme to filter tissue compartment based on differences in diffusivities and measures the recovery in diffusion measurements over an increasing mixing time, characterized by Apparent Exchange Rate (AXR). DKI(t), on the other hand, measures apparent exchange based on its effects on the asymptotic decay of diffusion kurtosis described by the Kärger model. In this study, we investigated the relationship between FEXI and DKI(t) based measurements of apparent exchange in post-mortem mouse brains and elucidated its confounding factors in determining the desired exchange process.
Date: February 24, 2021, at 2:00 p.m.
Location: via Webex
Clinical Assistant Professor, Radiology
Director, Cardiac MRI Service, Cardiothoracic Radiology Division
University of Michigan
The parallel growth of obesity and diabetes has escalated over the last four decades placing over 1.9 billion overweight and obese individuals at increased risk of developing cardiovascular disease (CVD). This risk has been attributed to the pressure of a low-grade inflammatory state, but the mechanism underlying the inflammation is unclear. An increased epicardial adipose tissue volume or thickness quantified by echocardiography, computed tomography (CT) or magnetic resonance (MR) has been shown to correlate with cardiovascular disease and diabetes independent of anthropometric measurements such as body mass index. However, in visceral obesity, epicardial adipose tissue can assume a white adipose phenotype that is hypothesized to be associated with proinflammatory markers. The white adipose tissue may precede the accumulation of fat and increase in epicardial adipose volume. The objectives for this talk are to discuss the current theories of defining cardiovascular or cardiometabolic risk, what research has been done by others and our group that could leverage future utilization of imaging as a surrogate marker of identifying patients at risk for adverse CVD outcome. We will emphasize the research performed by our group to understand the correlation between the increased epicardial adipose fat fraction quantified by water-fat imaging and coronary artery disease including tissue inflammation defined by lipidome and transcriptome profiling in patients undergoing open-heart surgery. Epicardial, extrapericardial, and subcutaneous depots expressed different imaging, lipidome and transcriptome signatures. Furthermore, increased epicardial fat fraction positively correlated with coronary artery disease, tissue ceramides, pro-inflammatory lipids, and proinflammatory gene expressions. We will discuss research questions and future direction of utilizing epicardial fat fraction to risk stratify CVD patients and monitor therapeutic response.
Date: February 16, 2021, at noon
Location: via Webex
PhD Candidate in Biomedical Imaging & Technology
Lazar Translational Brain Imaging Lab
Vilcek Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Previous research has suggested both perfusion and free water (FW) alterations in Psychotic Spectrum Disorders (PSD), assessed independently of each other. To study PSD neuropathology, we applied a three-compartment IVIM-FWI model which disentangles FW diffusion and perfusion along with an anisotropic diffusion tissue compartment. The estimation of each of these metrics may be affected when the effects of the other are not taken into consideration. Previous histological studies have suggested an array of microvascular and microstructural deficits likely to impact perfusion and FW in PSD, including increased inflammation, morphological differences in capillaries, and disruptions in the neurovascular unit cells and the blood brain barrier. The aim of this research was to evaluate, for the first time, if the three-compartment IVIM-FWI model can describe microvascular and microstructural changes in PSD in both gray and white matter. Additionally, we examined the relationships between the IVIM-FWI derived measures of perfusion fraction (PF), FW, and fractional anisotropy of tissue (FAt) and psychosis duration, cognition, and MR spectroscopy metabolites.
Date: December 17, 2020, at 2:00 p.m.
Location: via Webex
PhD Candidate
Functional MRI Lab
University of Michigan
Pulse design and reconstruction are two important topics in MR research for enabling faster imaging. On the pulse design side, selective excitations that confine signals to be within a small ROI instead of the full imaging FOV can promote sampling sparsity in the k-space, as a direct outcome of the change of the corresponding Nyquist sampling rate.
On the reconstruction side, besides improving algorithms’ capability on restoring images from less data, another objective is to reduce the reconstruction time, particularly for dynamic imaging. This talk presents our developments on these two perspectives: The first part introduces a pulse design framework built on our efficient auto-differentiable Bloch simulator. By propagating the derivatives in an automatic way, this tool connects excitation objectives (e.g., accuracy) directly to the pulse waveforms to be designed without approximations such as the small-tip model. It enables us to address excitation losses that are previously not approachable. We apply this tool on outer volume saturated inner volume imaging, which confines imaging signals into an ROI by selectively spoiling spin magnetizations outside.
Date: November 24, 2020, at 10:00 a.m.
Location: via Webex
PhD Candidate
Max-Delbrück-Centrum für Molekulare Medizin
Berlin Ultrahigh Field Facility
Renal tissue hypoxia is considered to be an important factor in the development of numerous acute and chronic kidney diseases. Blood oxygenation sensitized MRI can provide quantitative information about changes in renal blood oxygenation via mapping of T2*. Simultaneous MRI and invasive physiological measurements in rat kidneys demonstrated that changes in renal T2* do not accurately reflect renal tissue oxygenation under pathophysiological conditions. Confounding factors that should be taken into account for the interpretation of renal T2* include renal blood volume fraction and tubular volume fraction. Tubuli represent a unique structural and functional component of renal parenchyma, whose volume fraction may rapidly change, e.g., due to alterations in filtration or tubular outflow.
Diffusion-weighted imaging (DWI) provides a method for in-vivo evaluation of water mobility. In the kidneys intravoxel incoherent water motion may be linked to three different sources: i) renal tissue water diffusion, ii) blood perfusion within intrarenal microvasculature and iii) fluid in the tubules. The latter provides means to probe for changes in the tubular volume fraction. Recognizing this opportunity this presentation examines the feasibility of assessing tubular volume fraction changes using the non-negative least squares (NNLS) analysis of DWI data.
Date: October 29, 2020, at 9:00 a.m.
Location: via Webex
Associate Professor of Radiology
MIR, Mallinckrodt Institute of Radiology
Washington University School of Medicine in St. Louis
No abstract was provided for this talk.
Date: October 28, 2020, at 2:00 p.m.
Location: via Webex
Assistant Professor
Department of Radiology
Massachusetts General Hospital
This talk will explore new ways to use local magnetic field control besides conventional “B0 shimming”. Perturbations of the main magnetic field (“B0”) due to tissue susceptibility interfaces are a long-standing obstacle in Magnetic Resonance applications. Inhomogeneous B0 fields can lead to artifacts such as geometric distortion, signal voids, poor RF pulse performance, and spectral line broadening. This has limited the use of diffusion, functional, and spectroscopic MR imaging in many regions of the brain and body. Recently, it has been shown that multi-coil arrays of independently-driven loops placed close to the body can generate nonlinear, high spatial-order field offsets to “shim out” unwanted susceptibility fields on a subject-specific basis, benefiting field homogeneity and image quality. In this talk, we explore the potential for repurposing multi-coil shim arrays for new applications that exploit their nonlinear, rapidly-switchable local field offsets. Examples include tailored field offsets for improved lipid suppression in MR spectroscopic imaging; zoomed functional MRI of target brain anatomy; flip angle correction at ultra-high field; and supplementary spatial encoding for improved parallel imaging. We will also explore ways to add local field control capability to coil arrays originally designed for other applications, such as RF receive arrays and Transcranial Magnetic Stimulation probe arrays, so that their degrees of freedom can be brought to bear.
Date: October 21, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor
University of British Columbia
The presentation will provide a broad overview of the history of myelin water imaging in humans. Myelin water imaging is based on measurement of the short T2 component of water in brain and spinal cord tissue. What began as a lengthy single slice, single center measurement has expanded to many countries on multiple continents in just over 25 years. Important work along the way has included post-mortem validation studies in human CNS tissue, comprehensive assessment of development and normal characterization in adults, as well application to many neurological diseases including multiple sclerosis, concussion, stroke and beyond. The creation of normative atlases and development of faster analysis approaches promises to help move myelin water imaging to clinic in the coming decade.
Date: September 30, 2020, at 2:00 p.m.
Location: via Webex
PhD Candidate in Translational Neuroscience
Graduate Research Fellow
Sastry Foundation Advanced Imaging Laboratory
Department of Psychiatry and Behavioral Neurosciences
Wayne State University
Detroit, MI
Parkinson disease (PD) is a neurodegenerative disorder characterized pathologically by nigrostriatal dopaminergic terminal loss and the development of Lewy pathology in surviving neurons of the substantia nigra (SN). Lewy pathology extends beyond the SN, and can be found in limbic and prefrontal cortical regions associated with cognitive decline. In vivo assessment of cortical microstructure and the extent of pathological changes will be clinically useful to monitor disease progression. For this purpose, our study used two diffusion MRI models, diffusion tensor imaging and neurite orientation dispersion and density imaging, to study the microstructural changes in the cerebral cortex of PD participants (n=18) compared to healthy controls (n=8). We demonstrate that in the absence of cortical thinning, PD pathology is associated with significant abnormalities in cortical diffusion metrics. Specifically, we found that the anterior cingulate cortex and inferior temporal lobe are consistently involved in PD through reductions in the intracellular volume fraction, fractional anisotropy (FA) and increased orientation dispersion index. FA reductions were extensive and involved more limbic areas such as entorhinal cortex, parahippocampus and insula. These findings are consistent with the presence of Lewy pathology in limbic regions and might be reflecting the earliest stages of tissue involvement in PD.
Date: September 29, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor
Department of Radiology
University of Michigan
Cardiovascular Magnetic Resonance (CMR) is a valuable tool that enables non-invasive characterization of tissue and assessment of cardiac function. Parametric mapping techniques play an important role in CMR due to their sensitivity to physiological and pathological changes in the myocardium. The capability of mapping T1 and T2 simultaneously in a single scan makes the novel cardiac Magnetic Resonance Fingerprinting (cMRF) technique a promising technology to facilitate diagnosis and treatment evaluation in various cardiac diseases. Unlike conventional parametric mapping approaches which may yield different T1 or T2 values for the same subject depending on the specifics of the MRI system hardware or pulse sequence implementation, cMRF has the potential to offer reproducible measurements of tissue properties on all MRI scanners. This talk aims to introduce the basics of the cMRF technique, including pulse sequence design, dictionary generation, and pattern matching, as well as highlighting potential applications.
Date: September 22, 2020, at 2:00 p.m.
Location: via Webex
Professor, Director C.J. Gorter Center for High Field MRI
Department of Radiology
Leiden University Medical Center
Commercial magnetic resonance imaging (MRI) systems cost millions of euros to purchase, require large electromagnetically shielded spaces to house, are extremely expensive to maintain and require highly trained technicians to operate. These factors together means that their distribution is confined to centrally-located medical centres in large towns and cities. Globally over 70% of the world’s population has absolutely no access to MRI, and clinical conditions which could benefit from even very simple scans cannot be treated. In the financially developed world, although MRI is diagnostically very important, the high cost and fixed nature prohibits any type of role in widespread health screening, for example. The magnetic fields typically used are very high, which means that there are severe contraindications so that, for example, MRI cannot currently be used in the emergency room. From the considerations above it is clear that if low-field MRI could be made more portable, accessible and sustainable then it would open up new opportunities in both developed and developing countries.
Rather than designing a highly sophisticated and expensive piece of equipment that can be used for all types of scanning, we use the philosophy of tailored design, such that we can design much more inexpensive systems for specific medical applications. Thus rather than one large MRI, the model is similar to having tens of different mobile ultrasound machines in a medical facility. In order to achieve portability, we design systems that use thousands of very small low-cost permanent magnets, arranged in designs which have no fringe field and therefore very easy siting requirements. The low magnetic fields allow scanning of patients with implants, and the scanner could potentially be transported on an ambulance for differentiation of hemorrhagic or ischemic stroke, for example. This talk will cover aspects of magnet, gradient and RF coil design for low fields (~50 mT), as well as corrections for gradient- and B0-distortions, and present the latest in vivo results as well as an outlook on future developments.
Date: August 5, 2020, at 2:00 p.m.
Location: via Webex
Assistant Professor/Emerging Scholar of Electrical and Computer Engineering
Engineering Division
NYU Abu Dhabi
There is a pressing need to identify deterioration amongst patients with COVID-19 in order to avoid life-threatening adverse events. Chest radiographs are frequently collected from patients presenting with COVID-19 upon arrival to the emergency department, since it is considered as a first-line triage tool and the disease primarily manifests as a respiratory illness. In this talk, I will discuss the AI prognosis system we developed using data collected at NYU Langone Health to predict in-hospital deterioration, defined as the occurrence of intubation, mortality, or ICU admission. In particular, our system consists of an ensemble of an interpretable deep learning model to learn from chest X-ray images and a gradient boosting model to learn from routinely collected clinical variables, e.g. vital signs and laboratory tests. The system also computes deterioration risk curves to summarise how the risk is expected to evolve over time. The results of retrospective validation on the held-out test set, the reader study, and silent deployment in the hospital infrastructure highlight the promise of our AI system in assisting front-line workers through real-time assessment of prognosis.
Date: July 22, 2020, at 2:00 p.m.
Location: via Webex
Assistant Professor
Athinoula A. Martinos Center for Biomedical Imaging
Department of Radiology
Massachusetts General Hospital, Harvard Medical School
Less is known about the structure-function relationship in the human brain than in any other organ system. The challenge of studying brain structure is that brain networks span multiple spatial scales, from individual neurons all the way to whole-brain systems. Diffusion magnetic resonance imaging (MRI) holds great promise among noninvasive imaging methods for probing cellular structure of any depth and location in the living human brain. Robust methods for in vivo mapping of tissue microstructure by diffusion MRI remain elusive due to the demand for fast and strong diffusion-encoding gradients. I will present an overview of our group’s efforts to advance MR hardware, biophysical modeling, and validation of microstructural metrics derived from diffusion MRI in order to probe the structure of the human brain across multiple scales. I will review current progress and applications of these methods to study axonal microstructure in the normal and aging human brain and assess axonal damage in multiple sclerosis.
To bridge the divide between the neuroscientific and clinical use of MRI in probing tissue microstructure, this presentation will also provide an overview of our ongoing efforts to optimize, translate and validate novel encoding and reconstruction techniques for the ultrafast acquisition of high-resolution, multi-contrast MR images in a clinical setting. These efforts are exemplified in our recent work exploring the benefits of improved speed and resolution of ultrafast susceptibility-weighted imaging to study microvascular injury in patients with severe COVID-19 using radiologic-pathologic correlative examinations.
Date: July 15, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor
Radiology Department and Advanced Imaging Research Center
UT Southwestern Medical Center
Recently, methods employing single- and dual-frequency saturation are gaining recognition to detect events on microstructural and molecular level. Specifically, Chemical Exchange Saturation Transfer (CEST) employs selective saturation of the exchanging protons and subsequent detection of the water signal decrease to create images that are weighted by the presence of a metabolite or pH. Here, we will describe aspects of translating CEST to reliable clinical applications at 3 Tesla and discuss its potential uses in human oncology, specifically breast cancer. Second, we will discuss a method called inhomogeneous Magnetization Transfer (ihMT), which employs dual-frequency saturation to create contrast originating from the residual dipolar couplings and thus specific to microstructure. We will focus on principles of ihMT, its comparison to other white matter metrics (diffusion) and the methods application to the detection of myelin in brain and spinal cord.
Date: May 27, 2020, at 11:00 a.m.
Location: via Webex
Senior Research Scientist
High-Field MR Center
Max Planck Institute for Biological Cybernetics
Tubingen, Germany
Due to a substantial shortening of the RF wave length (below 15 cm at 7T), RF magnetic field at UHF has a specific transmit (Tx) excitation pattern with strongly decreased (more than 2 times) values at the periphery of a human head. This effect is seen not only in the transversal slice but also in the coronal and sagittal slices, which considerably limits the longitudinal Tx-coverage (along the magnet’s axis) of conventional surface loop head arrays. In this work, we developed a novel human head UHF array consisted of 8 transceiver folded-end dipole antennas circumscribing a head. Due to the asymmetrical shape of the dipoles (bending and folding) and the presence of an RF shield near the folded portion, the array simultaneously excites two modes, i.e. a circular polarized mode of the array itself, and the TE mode (“dielectric resonance”) of the human head. Mode mixing can be easily controlled by changing the length of the folded portion. Due to this mixing, the new dipole array improves longitudinal coverage as compared to unfolded dipoles. By optimizing the length of the folded portion, we can also minimize the peak local SAR value and decouple adjacent dipole elements.
Date: May 20, 2020, at 2:00 p.m.
Location: via Webex
Associate Dean for Mentoring and Professional Development
Professor and Senior Vice Chair of Radiology
Vice Chair of Academic Affairs
Director, Clinical Faculty Mentoring
NYU Langone Health
This lecture will provide a brief clinical overview of SARS-CoV-2 infection and COVID-19 manifestations in the lungs. Imaging findings in the chest will be defined and literature reports summarized. Our evolving clinical experience will be described, including the subacute and chronic manifestations of COVID-19 lung disease we are now seeing. Finally, completed and ongoing thoracic COVID research projects will be presented.
Date: May 13, 2020, at 2:00 p.m.
Location: via Webex
Director, Microstructure Imaging Lab
Assistant Professor of Radiology – Division of Neuroradiology
University of Iowa
Magnetic Resonance Imaging has revolutionized the field of neuroscience by providing a non-invasive means to study the brain, to understand its organization, specialization, and anomalies in an unprecedented manner. Despite the rapid advances in MRI instrumentation, it is still challenging to achieve high-quality data in an efficient manner for several MR imaging modalities, especially for those modalities involving multi-dimensional imaging. In this talk, I will discuss several computational approaches that we have developed to achieve high efficiency MR imaging to enable many applications. These approaches strive to achieve high resolution, high SNR, and artifact-free MRI by jointly optimizing the contribution of MR acquisition, the signal modeling under investigation, and the reconstruction methods to provide meaningful information in an efficient manner. In this talk, I will focus the discussion mainly on diffusion magnetic resonance imaging and our work towards improving the efficiency of this modality.
Merry Mani received her PhD in 2014 from the University of Rochester, NY. Later in 2014, she joined the Magnetic Resonance Research Facility at the University of Iowa as a post-doctoral research fellow, where she developed new imaging methods on the 7T MRI. In 2019, she became an Assistant Professor in the department of Radiology, Carver College of Medicine, University of Iowa. Her lab focuses on integrating cross-disciplinary tools such as signal modeling and signal processing with imaging physics and image analysis tools to enable high efficiency MRI. These include the development of novel pulse sequences and optimization of sampling trajectories and reconstruction methods for maximum performance.
Date: May 6, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor
Radiology and Biomedical Imaging
Yale University School of Medicine
Like standard gradients, nonlinear gradients modulate the magnitude of Bz as a function of position; the difference is that the magnitude as a function of position is generally not linear or unidirectional. One important consequence of gradient nonlinearity is that the modulation of spins is no longer sinusoidal, so MR data do not correspond to points in k-space. Therefore, early encoding strategies focused on optimizing sequences by considering encoding in the spatial domain. However, a k-space analysis of nonlinear encoding provides significant insights on sequence design and suggests novel strategies, such as FRONSAC encoding. With FRONSAC, most of the encoding comes from a standard linear trajectory (e.g. Cartesian, radial or spiral), but nonlinear gradients are used to effectively increase the width of the k-space trajectory. For an undersampled scan, the additional width reduces gaps in k-space and improves reconstructions, but most other properties of the underlying linear method are unchanged. For example, Cartesian-FRONSAC retains features like insensitivity to off-resonance spins and timing delays, ease of changing FOV, resolution, and orientation, and relatively simple contrast behavior, while still allowing for higher undersampling factors. This versatile approach can be added to nearly any sequence, improving undersampling artifacts even for low channel arrays, as we have shown by acquiring a full FRONSAC-enhanced brain protocol in a cohort of healthy subjects.
An additional emerging application of nonlinear gradients is in generating diffusion contrast. In some sense, a linear gradient is the maximally egalitarian way to distribute a ΔB(x): it generates the same Gx (d(ΔB)/dx) everywhere, but the peak Gx across the FOV is the lowest possible. By allowing nonlinearity, Gx is different at each voxel, but it can be concentrated to certain regions of interest. Thus, for specialized applications, it may be possible to achieve massive gradient strengths and very high diffusion weightings using simple equipment. For example, for prostate DWI, we propose an inside-out nonlinear gradient, which simulations suggest will ultimately double CNR in ADC maps.
Date: April 29, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor and Associate Chair of Graduate Education
Department of Biomedical Engineering
University of Michigan
Wouldn’t it be great to perform a surgery without incision or bleeding? “Histotripsy” is the first non-invasive, non-ionizing, and non-thermal ablation technique that is invented by Dr. Xu and her colleagues at the University of Michigan. Using ultrasound pulses applied from outside the body and focused to the target diseased tissue, histotripsy produces a cluster of energetic microbubbles at the target tissue using the endogenous gas pockets with millimeter accuracy. These microbubbles, each similar in size to individual cells, function as “mini-scalpels” to mechanically fractionate cells to acellular debris in the target tissue. The acellular debris is absorbed over time via metabolism, resulting in effective tissue removal. Off-target tissue remains undamaged and no incision is needed. Thus histotripsy can perform non-invasive surgery guided by real-time imaging. Histotripsy has potential for many clinical applications where non-invasive tissue removal is desired. Recent research in Dr. Xu’s lab also shows potent immune response and abscopal effects induced by histotripsy and its potential for immunotherapy. Dr. Xu will talk about the mechanism and instrumentation development of histotripsy as well as the latest pre-clinical and clinical studies of histotripsy for cancer, neurological, cardiovascular, and immunotherapy applications.
Zhen Xu is a tenured Associate Professor and Associate Chair of Graduate Education at the Department of Biomedical Engineering at the University of Michigan, Ann Arbor, MI. She received the Ph.D. degree in biomedical engineering from the University of Michigan in 2005. Her research focuses on ultrasound therapy and imaging, particularly histotripsy. She received the IEEE Ultrasonics, Ferroelectrics, and Frequency Control (UFFC) Outstanding Paper Award in 2006; National Institute of Health (NIH) New Investigator Award at the First National Institute of Biomedical Imaging and Bioengineering (NIBIB) Edward C. Nagy New Investigator Symposium in 2011, The Federic Lizzi Early Career Award from The International Society of Therapeutic Ultrasound (ISTU) in 2015, the Fellow of American Institute of Medicine and Bioengineering in 2019, and The Lockhart Memorial Prize for Cancer Research in 2020. She is an associate editor for IEEE Transactions on UFFC and Frontiers in Bioengineering and Biotechnology, Deputy VP of UFFC Ultrasonics Standing Committee, and an elected board member of ISTU. She is a principal investigator of grants funded by NIH, Office of Navy Research, American Cancer Association, and Focused Ultrasound Foundation. She is also co-founder of HistoSonics, a startup company developing histotripsy for oncological applications.
Date: April 22, 2020, at 2:00 p.m.
Location: via Webex
Principal Investigator
Center for Research in Computer Vision (CRCV)
University of Central Florida
Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this talk, I will share our unique experience for developing a paradigm shifting computer aided diagnosis (CAD) system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. In other words, we are creating artificial intelligence (AI) tools that get benefits from human cognition and improve over complementary powers of AI and human intelligence. We first developed an eye-tracking interface providing radiologists with a real radiology reading room experience. Second, we proposed a novel computer algorithm that unifies eye-tracking data and a CAD system. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve their diagnostic decisions. The proposed C-CAD system has been tested in a lung and prostate cancer screening experiment with multiple radiologists. More recently, we also experimented brain tumor segmentation with the proposed technology leading to promising results. In the last part of my talk, I will describe how to develop AI algorithms which are trusted by clinicians, namely “explainable AI algorithms”. By embedding explainability into black box nature of deep learning algorithms, it will be possible to deploy AI tools into clinical workflow, and leading into more intelligent and less artificial algorithms available in radiology rooms.
Date: April 15, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor, Harvard Medical School
Associate Investigator, Massachusetts General Hospital
Athinoula A. Martinos Center for Biomedical Imaging
This talk will provide an overview of work that our group has done on mapping connectional anatomy from diffusion MRI, and a preview of where this path might lead us next. First, I will discuss our previously developed algorithms for reconstructing white-matter pathways from diffusion MRI. These include both supervised and unsupervised methods with a common theme: like neuroanatomists, they define white-matter bundles based on relative position with respect to neighboring anatomical structures, rather than based on absolute coordinates in a template space. This makes them robust to individual variability and to the effects of disease or healthy development and aging.
Second, I will present results from recent post mortem validation studies, where we have evaluated the accuracy of diffusion MRI with respect to polarization-sensitive optical coherence tomography in human samples, or chemical tracing in non-human primates. Our results suggest that existing methods for inferring the orientation of axon bundles from diffusion MRI do not benefit substantially from very high b-values. This implies that our analysis tools have not kept up with the rapid progress of our hardware, and that new tools are needed to fully take advantage of the data that can be acquired by today’s ultra-high-gradient MRI scanners. I will end the talk by discussing how we may be able to address this, by using the post mortem data not only to evaluate existing methods but to engineer the next generation of tractography algorithms.
Date: April 8, 2020, at 2:00 p.m.
Location: via Webex
PhD Student
Biomedical Imaging and Technology Program
Sackler Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Myelin abnormalities in schizophrenia spectrum disorders have been suggested by histological studies, which have shown aberrations in myelin lamellae, oligodendrocyte structure, and myelin- and oligodendrocyte-related gene expression. However, in vivo examination of myelin content, especially the intra-cortical myeloarchitecture remains limited. In our current project, we employ magnetization transfer imaging to derive macromolecular proton fraction (MPF), a quantitative estimate of myelin content. This talk will focus on data suggesting a flattening of the cortical myelin profile in patients with schizophrenia spectrum disorders and an association of cortical myelin alterations with illness progression and cognitive outcomes. Preliminary findings on whole-brain myeloarchitectural similarity changes in schizophrenia will also be presented.
Yu Veronica Sui is a second-year graduate student in Sackler Institute’s Biomedical Imaging and Technology training program working with Mariana Lazar. She has a background in cognitive psychology and is interested in developing and employing new imaging and analytics methods to characterize the neural bases of psychiatric disorders. Her focus in Lazar Lab is psychosis-related pathological changes in the brain, including both microstructural and connectivity abnormalities.
Date: February 11, 2020, at noon
PhD Student
Biomedical Imaging and Technology PhD Training Program
Sackler Institute of Graduate Biomedical Sciences
NYU Langone Health
No abstract was provided for this talk.
Date: February 7, 2020, at noon
Director, MRI Program
National Heart, Lung, and Blood Institute (National Institutes of Health)
Lower field strength MRI systems paired with high-performance hardware and advanced imaging methods offer unique opportunities for clinical imaging. Specifically, this system configuration offers improved safety for MRI-guided invasive procedures, improved imaging in high-susceptibility regions including the lung, and advantages for efficient image acquisitions. In light of developments in MRI engineering and available computational power, and as well as the drive to reduce MRI costs, there is significant value in revisiting lower field MRI in the context of modern clinical imaging. This talk will describe the experience of the NHLBI imaging patients on a ramped down 0.55T system for 2 years.
Dr. Adrienne Campbell-Washburn is the Director of the MRI Technology Program at the National Heart, Lung, and Blood Institute (National Institutes of Health). Her research focuses on the development of MRI technology for cardiac imaging, lung imaging, and MRI-guided interventions. She works on developing advanced MRI acquisitions that leverage non-Cartesian sampling and reconstruction methods using state-of-the-art computational resources in the clinical environment. Her research aims to improve SNR-efficiency, imaging speed, interventional procedural guidance including device safety and visibility, motion robustness, quantification, and clinical integration.
Date: January 28, 2020, at noon
Professor
Wayne State University School of Medicine
Imaging biomarkers that bridge neuronal abnormalities in vivo and behavior, and animal models and human patients, are urgently needed to quicken the discovery and application of novel disease-modifying therapy, but are not yet available. I will be discussing novel MRI and OCT approaches for measuring sustained and excessive production of free radicals (i.e., oxidative stress) in neuronal laminae without a contrast agent in untreatable neurodegenerative disease. These studies set the stage for translating and managing anti-oxidant treatment in patients for the first time.
Date: January 21, 2020, at noon
Postdoctoral Fellow
Stanford University
Existing clinical infrastructures severely under-utilize modern computation resources, leading to costly errors, slow workflows, and limited research opportunities. However, trends in cloud computing and machine learning are rapidly changing this landscape. Tech companies, such as Amazon, Google, and Microsoft, are now racing to integrate high-performance computing into clinical settings. Medical imaging stands to gain tremendously from advances in computing power, which will enable many previously unthinkable applications.
In this talk, I will focus on three directions on leveraging these emerging computing resources to improve medical imaging: 1) reconstructions of high-dimensional volumetric dynamic MRI on the order of 100GBs; 2) continuous learning and image quality improvement from undersampled datasets; 3) optimizing end-to-end systems across clinical workflow.
Date: January 14, 2020, at noon
PhD Student, Biomedical Imaging and Technology
Sackler Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Dynamic MR image quality is limited by the temporal and spatial resolution trade-off. Adopting machine learning in the reconstruction network provides an alternative method to reconstruct the image of better quality. This presentation will focus on our work using Recurrent Neural Networks (RNN) as a regularizer in our dynamic MR reconstruction network. The regularizer is designed to take time series of k-space with flexible lengths and use it to reconstruct image series. I will show how we design simulations to get ground truth images for model training. Then I will show the output of the trained network on simulated breast perfusion MR data with a comparison to other reconstruction methods.
Zhengnan Huang joined Florian Knoll’s lab in 2018. He has an educational background in bioinformatics. His research interest is MR image reconstruction and machine learning applications.
Date: December 18, 2019, at noon
PhD Student
University College London
In this talk, we discuss three deep learning techniques to improve the image quality of 3D diffusion MRI images. We first introduce a novel low-memory method, which allows us to control the GPU memory usage during training, therefore allowing us to handle the processing of three-dimensional, high-resolution, multi-channeled medical images. Secondly, we present the first multi-task learning approach in data harmonization, where we integrate information from multiple acquisitions to improve the predictive performance and learning efficiency of the training procedure. Thirdly, we present an extension of the transposed convolution, where we learn both the offsets of target locations and a blur to interpolate the fractional positions. All three techniques can be applied in other image-related paradigms.
Date: December 17, 2019, at noon
Assistant Professor
Departments of Radiology & Biomedical Imaging, Psychiatry, and Biomedical Engineering
Yale University
Dysregulated immune signaling contributes to many neuropsychiatric conditions. Brain PET imaging can measure neuroimmune factors that inform treatment development for such conditions. This talk will focus on PET imaging of the 18-kDa translocator protein (TSPO). Preclinical work informing the interpretation of TSPO signal, including imaging dynamic responses to endotoxin, an acute immune stimulus, will first be presented. Next, human data imaging TSPO in tobacco smoking, alcohol use disorder, and Alzheimer’s disease will be presented to demonstrate diverse applications of these techniques. Whole body imaging of TSPO following acute alcohol administration as an immune stimulus will also be presented. Finally, work characterizing new radiotracers that complement TSPO measures in immune signaling will be presented. This work depicts ways in which PET imaging can be leveraged to study immune function in the context of neuropsychiatric disorders.
Date: December 13, 2020, at noon
Associate Professor
University of Nottingham
A novel investigation of rheo-markers (proton T2* and sodium multiple quantum filtering) shows the potential for multi-nuclear MRI biomarkers in mechanically loaded joints with good evidence of a dynamic 23Na environment during compression which may be useful for early OA detection before symptoms occur.
Date: December 10, 2019, at noon
Associate Scientist, Department of Radiology and Imaging
Associate Professor of Biomedical Imaging in Orthopaedic Surgery
Weill Cornell Medical College of Cornell University
New York, NY
A majority of primary total hip arthroplasty (THA) devices function well, but implant failures occur. This presentation will cover our long-standing efforts to utilize MRI in identifying patients needing premature implant revision due to adverse local tissue reactions (ALTRs). The utility of advanced multi-spectral imaging to reduce metallic susceptibility artifact and visualize synovitis, osteolysis, and tendon tears near arthroplasty will be displayed. I will also show results from our ongoing studies using MRI to evaluate patients with different THA bearing materials to determine which factors are predictive of abnormal synovial reaction. Finally, data will be shown regarding the longitudinal prevalence of MRI-detected ALTRs in a cohort of high-functioning THA patients.
Date: December 5, 2019, at noon
Associate Professor
Departments of Molecular Physiology and Biophysics, Neuroscience and Radiology
Baylor College of Medicine
Houston, TX
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the neuropathological accumulation of amyloid beta (Ab) plaques and neurofibrillary tangles comprised of hyperphosphorylated tau. Tau is a microtubule-associated protein involved in microtubule stability, and when tau is hyperphosphorylated, microtubules become destabilized, which leads to impaired axonal transport. Axonal transport is an important cellular process that shuttles vesicles, neurotransmitters, and mitochondria from the soma to the synapse. Perturbations in axonal transport disrupt neuronal activity by reducing the transport of mitochondria, increasing reactive oxygen species (ROS), and diminishing the formation of active zones at the synapse. Axonal transport deficits are thought to occur early and continue to progress in AD. Thus, there is a significant need and strong scientific premise to identify the mechanisms by which axonal transport deficits occur and can also be improved in AD.
Olfactory receptor neurons are the only part of the central nervous system (CNS) with direct access to the outside world. They lie at the beginning of a neural network which projects to the olfactory bulb, followed by the piriform olfactory cortex (primary olfactory cortex) and the entorhinal cortex (secondary olfactory cortex). The olfactory system is also the first system affected in AD patients and mouse models of AD before cognitive deficits develop. Indeed, using Manganese Enhanced MRI (MEMRI), we have shown that axonal transport deficits in the olfactory receptor neurons occur before the appearance of learning and memory deficits and are reversed when we reduce ROS levels by overexpressing superoxide dismutase 2 (SOD-2) in AD mice. Here, we describe our current efforts with reducing oxidative stress in the olfactory structures in mouse models of AD with intranasally applied nanoantioxidants.
Date: December 5, 2019, at 10:00 a.m.
Associate Professor
Department of Neuroscience
Ruth and Bruce Rappaport Faculty of Medicine
Technion – Israel Institute of Technology
Functional MRI is used extensively in human brain research, enabling characterization of distributed brain activity underlying complex perceptual and cognitive processes. However, it has been limited in utility in rodents. I will present the work we have done to establish awake mouse MRI, characterize the properties of the hemodynamic response function as different from humans, and how these two aspects enabled us to conduct whole-brain fMRI of the behaving animal. I will expand on recent work using whole-brain functional imaging of head-fixed mice performing odor discrimination and conclude by showing additional behavioral modalities we develop with the goal to establish this approach as a platform to be used extensively in the field.
Date: November 25, 2019, at noon
Postdoctoral Fellow
Division of Medical Physics in Radiology
German Cancer Research Center
A novel imaging technique is presented, capable of simultaneously quantifying time-resolved blood flow velocities and the relaxation constants of static tissue. This is accomplished through the use of a Magnetic Resonance Fingerprinting (MRF) based approach. The developed technique, termed “Flow-MRF”, allows accurate mapping of velocities and relaxation constants in measurement times up to 4-fold shorter than conventional MRI-based velocimetry techniques.
Date: November 21, 2019, at noon
Assistant Professor
Department of Radiology
Johns Hopkins University
Chemical exchange saturation transfer (CEST) is a relatively new type of MRI contrast that indirectly detects low concentration labile protons through water signal with enhanced sensitivity. In this presentation, I will explain the principles of CEST imaging and its applications. I will show results from using CEST to image D-glucose (glucoCEST) in vivo, first on brain tumor mouse model at ultra-high magnetic field, then on human brain tumor patient on 7T system. Our recent effort of translating the technique to clinical field strength and the promise and challenges of glucoCEST at clinical field strength will be also discussed.
Date: November 12, 2019, at noon
Professor
Sant Pau Memory Unit
Barcelona, Spain
CSF, PET, and MRI multimodal studies enable the early diagnosis of Alzheimer’s Disease. We have proposed a model in which interactions between biomarkers in preclinical AD result in a two-phase phenomenon: an initial phase of cortical thickening due to amyloid-related inflammation, followed by a cortical atrophy phase which occurs once tau biomarkers become abnormal. These results have implications in the selection of patients for clinical trials and the use of MRI as a surrogate marker of efficacy. We will also present data showing the potential of studying the cortical microstructure with DTI to assess these early changes and in the diagnosis of other neurodegenerative diseases.
Date: October 24, 2019, at noon
Research Group Leader
Department of High-field Magnetic Resonance
Max Planck Institute
Tuebingen, Germany
In this talk, I will introduce the combination of the advanced fMRI method with the emerging neuro-techniques to decipher the neuro-glial-vascular (NGV) coupling basis of brain state dynamics. First, we will see through the large voxel acquired from conventional fMRI to decipher the contribution from distinct vascular components to the fMRI signal. A newly developed single-vessel fMRI method allows identifying the activity-evoked hemodynamic signal propagation through the cerebrovasculature in the brain with either sensory inputs or optogenetic activation. Second, we will combine the fMRI with the optical fiber-mediated calcium recordings to decipher the cell-type-specific contribution to the fMRI signal from neurons and astrocytes. Meanwhile, we will also show how extracellular glutamate can be recorded simultaneously to mediate NGV interaction. Finally, we are going to present how the global fMRI signal fluctuation can be linked to the brain state changes. We merge the pupillometry with the multi-modal fMRI to examine the detailed arousal index by pupil dynamics and fMRI fluctuation. In summary, we hope to provide a novel perspective to understand brain function with multi-modal fMRI across different scales.
Date: October 15, 2019, at noon
Assistant Professor
Human Brain Biophysics Lab
Edmond and Lily Safra Center for Brain Sciences (ELSC)
The Hebrew University of Jerusalem
Quantitative MRI (qMRI) parameters such as T1 provide physical parametric measurements crucial for clinical and scientific studies. However, an important challenge in applying qMRI measurements is their biological specificity, as they change in response to both molecular composition and water content. I will discuss an approach that disentangles these two important biological quantities and allows for decoding of the molecular composition from the qMRI signal. I will demonstrate that this approach can reveal the molecular composition of lipid samples. Furthermore, we identify region-specific molecular signatures in the human brain that have been validated against histological measurements. Last, we exploit our method to reveal region-specific molecular changes in the aging human brain. I suggest that the ability to disentangle molecular signatures from water-related changes opens the door to a quantitative and specific characterization of the human brain.
Date: October 8, 2019, at noon
Assistant Professor
Department of Neurology, Columbia University
Department of Biomedical Engineering, Columbia University
Taub Institute for Research on Alzheimer’s Disease and the Ageing Brain
This talk will start with a brief introduction of what is negative BOLD response in fMRI data and what are its characteristics. It continues by categorizing different types of negative BOLD signal according to their properties and outlines the optimal techniques used to extract negative BOLD response.
The applications of negative BOLD response are unlimited, however, three on-going research projects in our lab which extensively rely on negative BOLD response will be presented. First project uses negative BOLD response to demonstrate how spontaneous activity and task-evoked activity in the brain give rise to two spatially overlapping but temporally dissociable signals which both are manifested in fMRI data. Using these findings, the second project attempts to use negative BOLD response to demonstrate evidences for the hierarchical structure in the human brain functional networks. This is done by demonstrating that task-evoked negative BOLD response in the Default mode network is modulated by switching attention whereas the functional connectivity between the same network of regions remain intact. Finally, we introduce negative BOLD response as a new brain biomarker that could potentially differentiate between normal and pathological ageing brains.
Date: October 4, 2019, at noon
Postdoctoral Fellow
Universidad Nacional Autónoma de México
Instituto de Neurobiología Laboratorio de Conectividad Cerebral
Several multiple fiber methods have been proposed that seem to overcome the limitations of the diffusion tensor and methodologies aimed to provide information from the diffusion signal, but that are mostly suited for single fiber population regions. Although the majority of these multiple fiber methods were created with the primary purpose of improving tractography results, some of them are able to provide per-bundle dMRI derived metrics. However, biological interpretations of such metrics are limited by the lack of histological confirmation.
To this end, we developed a straightforward biological validation framework. Unilateral retinal ischemia was induced in ten rats, which resulted in axonal (Wallerian) degeneration of the corresponding optic nerve, while the contralateral was left intact; the intact and injured axonal populations meet at the optic chiasm as they cross the midline, generating a fiber crossing region in which each population has different diffusion properties. Five rats served as controls. High-resolution ex vivo dMRI was acquired five weeks after experimental procedures.
We correlated and compared histology derived information to per-bundle descriptors obtained from three multiple fiber methodologies for dMRI analysis: constrained spherical deconvolution (CSD) and two multi-tensor (MT) representations. We found a tight correlation between axonal density (as evaluated through automatic segmentation of histological sections) with per-bundle apparent fiber density (from CSD) and fractional anisotropy (derived from the MT methods). The multiple fiber methods explored were able to correctly identify the damaged fiber populations in a region of fiber crossings (chiasm). Our results provide validation of metrics that bring substantial and clinically useful information about white-matter tissue at crossing fiber regions.
Our proposed framework is useful to validate other current and future dMRI multiple fiber methods; it also can be extended for the analysis of other pathological conditions, such as inflammation and demyelination, in order to evaluate the capabilities of these dMRI methods to differentiate between.
Date: October 1, 2019, at noon
Postdoctoral Researcher
Athena Lab
Inria Sophia Antipolis, France
Elucidating the relationship between the structure and function of the brain is one of the main open questions in neuroscience. The capabilities of diffusion MRI-based (dMRI) techniques to quantify connectivity strength between brain areas, referred to as structural connectivity, in combination with modalities to quantify brain function such as electrocorticography (ECoG) have enabled advances in this field.
The aim of the project that I will talk about is to establish a relationship between dMRI-based structural connectivity and effective connectivity maps based on the propagation of Cortico-Cortical Evoked Potentials (CCEPs). To this end, we applied direct electrical stimulation of the cortex during awake surgery of brain tumor patients and recorded the induced electrophysiological activity with subdural ECoG electrodes.
I will briefly summarize our study of seven patients. For each of them, we correlated dMRI-based structural connectivity measures, including streamline counts and lengths, with delays and amplitudes of CCEPs. In addition, we used the structural information to predict the CCEP propagation with a linear regression model.
Date: September 19, 2019, at noon
Assistant Professor of Neurology
USC Stevens Neuroimaging and Informatics Institute
Keck School of Medicine
University of Southern California
Arterial spin labeling (ASL) is a non-invasive MRI technique for cerebral blood flow (CBF) measurement by using magnetically labeled blood spins as endogenous tracers. The recent development of ASL has promoted it as a useful imaging tool for tissue perfusion assessment in cerebrovascular disorders. For perfusion imaging, after spin tagging, images are generally acquired at a relatively long post-labeling delay time (~1.8s) when the labeled blood from labeling plane reaches capillaries/tissue. Additional physiological information can be derived during the passage of labeled blood through the cerebral arterial trees into capillaries and tissue, such as dynamic MR angiography, vascular territorial mapping, cerebral blood volume (CBV) and vascular compliance, et al., all of which also provide useful information in the diagnosis and treatment of cerebrovascular disease. In this talk, I will introduce my work about these recent advances in ASL beyond CBF measurement.
Date: September 5, 2019, at noon
Siemens Healthcare GmbH
Diagnostic Imaging
Inge Brinkmann will provide an overview of her work at Siemens Healthineers.
Date: September 3, 2019, at noon
PhD Candidate
Computer Science
Federal University of Pernambuco
Recife, Brazil
In recent years, deep convolutional neural networks have overcome several challenges in the field of computer vision and image processing. In particular, pixel-level tasks such as image segmentation, restoration, generation, enhancement, and inpainting, showed significant improvements thanks to advances in the technique. In general, the supervised training of a neural network entails solving a high dimensional non-convex optimization problem whose objective is to transform the vectors of the input domain to a prescribed output. However, due to the high dimensionality of the parameter space and the presence of saddle points and large flat regions on the error surface, the process of training a neural network is extraordinarily challenging. We propose modeling new loss functions to facilitate training while improving the generalization of models for pixel-level regression and classification tasks. Our newly introduced loss functions modify the optimization landscape to achieve better results in regions which are notoriously more prone to failure. They increase the overall optimization performance and accelerate convergence. We applied our formulations to instance segmentation of cells with full and weak supervision and tested them on challenging biological images with isolated and cluttered cells. We also propose a new pixel-level regression loss function applied to the multi-focus image fusion problem resulting in the joint learning of activity level measurement and fusion rule. New pre-processing and post-processing techniques to help improve the solutions are also introduced. Our methods have shown significant improvements in the segmentation and image restoration tasks as reported by a diverse set of metrics and visual inspections.
Date: August 29, 2019, at noon
PhD Candidate
Computer Science Engineering
Ghent University
Belgium
Recent achievement of deep learning algorithms using convolutional neural networks (CNNs) yields high performance of image classification and segmentation. The algorithms have been applied to assist doctor’s medical decision more efficiently and effectively. In this talk, I will introduce deep learning applications to rotator cuff tears, glaucoma, and intraocular pressure relations with daily diet pattern.
Date: August 27, 2019, at noon
Senior Research Scientist
Co-Director of OLE! (Osteoarthritis Lab for Experimental Imaging)
Department of Radiology
NYU Langone Health
Osteoarthritis (OA) is the most common form of arthritis, affecting millions of people in the US for which only palliative treatments are available until joint replacement surgery. The elusiveness of effective OA treatments is the consequence of OA being a complex disease. OA is a multifactorial disease with inflammatory, metabolic, and mechanical causes involving all tissues of the joint. Thus, we still lack understanding on OA pathogenesis, in part due to the lack of diagnostic biomarkers that can detect early pathological changes in the joint and monitor therapy. A major barrier in OA research is to see and understand the interplay between OA factors to both be able to phenotype OA and provide patient-specific treatments. At OLE! (Osteoarthritis Lab for Experimental Imaging), we aim to solve this technological problem by developing advanced imaging technology that can monitor in vivo the influence of OA factors and treat them. We have established an innovative research program for in vivo molecular imaging of the degenerative joint. We are developing imaging probes with theragnostic potential that combine the specificity of biochemical assays with anatomical and tissue-specific assessment of early degenerative changes.
Date: August 22, 2019, at noon
Postdoctoral Research Scientist
Siemens Healthineers USA
Conventional T1- and T2- weighted pulse sequences are routinely used in the clinic for the diagnosis of a variety of pathologies. Quantitative estimation of tissue relaxation times can be used to further improve the quality of diagnosis in applications including cardiac, abdominal, and musculoskeletal imaging. In this talk, I will introduce a radial Turbo Spin Echo (RADTSE) pulse sequence for simultaneous T2w imaging and T2 mapping. Specifically, I will present a RADTSE pulse sequence with very long echo train lengths and variable refocusing flip angles for improved slice coverage in abdominal breath-held imaging. I will also discuss a simultaneous multi-slice excitation technique to improve the slice and SNR efficiency of double inversion RADTSE for cardiac imaging. Finally, I will give an overview of my ongoing research on quantitative T1 mapping and the use of artificial intelligence for analysis of deep brain structures.
Date: August 20, 2019, at noon
Principal Investigator
Department of Chemical Physics
Weizmann Institute of Science
Magnetic Resonance Spectroscopy (MRS) is used to non-invasively monitor the in-vivo biochemistry of tissue, by quantifying the concentrations of several prominent metabolites, including glutamate, choline, GABA, and creatine, among others. Conventional MRS produces static estimates of concentrations. In this talk, I will present two recent advances in MRS methodology which provide more dynamic information. First, I will discuss our work on multiparametric MRS, which simultaneously quantifies metabolite concentrations and relaxation times (T1, T2). Both T1 and T2 provide information about the molecular microenvironment of the metabolites via their microscopic dynamics. In the second part of the talk, I will discuss our work on functional MRS, which examines the temporal changes to several prominent metabolites in response to external stimuli, and discuss some of our interpretations of the changes measured in this unsolved, fascinating puzzle.
Date: August 13, 2019, at noon
Staff Scientist
Research Department at Siemens Molecular Imaging
Knoxville, TN
In this talk, I will introduce my recent research activities from image formation to post-processing using examples of whole body scatter estimation and image reconstruction for Biograph mMR and a deep learning powered lung analysis post-processing application. For the Biograph mMR, we designed a new method to process step and shoot sinogram to simulate a whole body sinogram and reconstruct the whole body image directly, which increases the quantitative accuracy of scatter estimation and improves the performance of image reconstruction. For post-processing, I will showcase several AI predevelopment activities, focusing on the lung ventilation/perfusion application. Here, deep learning-based lung lobe segmentation has been developed to enable a potentially fully automated workflow for lung analysis. This prototype is available on the Siemens Frontier platform, offering a seamless integration to syngo.
Date: August 6, 2019, at 4:00 p.m.
Applied Research Scientist
Facebook AI Research (FAIR)
fastMRI is a collaborative research project between Facebook AI Research (FAIR) and NYU Langone Health. The aim is to investigate the use of AI to improve acceleration and robustness of MRI scans. In this talk, Tullie, a Research Engineer at FAIR, will give an overview of the work done on knee image reconstruction and reinforcement learning-based active sampling. He will cover the plans going forward to investigate brain image reconstruction, motion-robust reconstructions for Dynamic MRI, and extensions to the active sampling work.
Date: August 6, 2019, at noon
Head, Bio-Computing Unit (BCU)
National Center of Biotechnology
Madrid, Spain
Expecting to fully engage equally deep Physicists and Biologists, I will introduce the notion of “how good a macromolecular CryoEM map is,” addressing this question in a totally new way in the field, by providing a “resolution tensor” per CryoEM voxel map (instead of just a number, the so-called “local resolution”). The mathematical beauty of this tensor representation will immediately open a new university of opportunities for experimentalists in CryoEM (clearly impacting Pharma), with the capability to assess the quality of the map from the map itself (without the images), the alignment errors, the presence of problematic directions… and much more.
Date: August 1, 2019, at noon
Professor of Medical Physics and Psychiatry
Co-Director of Waisman Brain Imaging Lab
University of Wisconsin-Madison
T1-weighted structural imaging with MP-RAGE is a cornerstone of brain imaging studies for both clinical and research applications. However, it is sensitive to head motion, RF inhomogeneities, and provides only a single image contrast. Recently, we developed MPnRAGE which combines inversion magnetization preparation with a 3D radial rapid gradient echo readout. This sampling enables the simultaneous acquisition of n inversion recovery contrasts, which may be used to generate one or more application-specific contrast images, and generate high-resolution, whole-brain T1 relaxometry images. The 3D radial sampling is also highly amenable to self-navigated motion correction during the reconstruction, which provides robust and reliable high-quality T1-weighted and quantitative T1 images of the brain. This technique is highly promising for brain imaging studies of children, aging, and brain pathology.
Date: July 23, 2019, at noon
Associate Professor
Harvard Medical School
A synergistic approach in developing MRI acquisition through utilizing the interplay between hardware design, software algorithm development, and MR physics has dramatically increased MRI’s spatiotemporal resolution capability. In this talk, I will cover some of these tailored acquisition strategies which are being pioneered by my group, focusing particularly on applications in rapid imaging, diffusion, & fMRI, and quantitative and multidimensional/time-resolved imaging of the brain. The overarching theme is in radically improving the speed, sensitivity, and specificity of in vivo brain imaging, with the goal of providing more detailed information about the brain both in health and disease.
Date: July 16, 2019, at noon
Associate Professor, Advanced Imaging Research Center
Associate Professor, Department of Radiology
University of Texas Southwestern Medical Center
Dr. Ren will discuss a series of studies using dynamic and kinetic MRS, that have identified cellular energetic activities in multiple pathways. He will also demonstrate how 7T 31P MRS can serve as a powerful tool to capture aberrant brain events in remote skeletal muscle.
Date: July 15, 2019, at noon
PhD Candidate
Florida State University
Migraine is a disabling, multifactorial recurrent neurological disorder. Affecting approximately 38 million people in the United States alone, migraine is recognized by the World Health Organization as the 7th most disabling condition, due to the sufferer’s inability to perform everyday activities. The characterization, classification, and diagnosis of migraine is complex due to the tremendous cohort of variable clinical triggers and symptoms reported. Collectively, the symptoms accompanying migraine implicate multiple neural networks and processes functioning abnormally. A mechanistic search for a common denominator based on the symptoms in migraine potentially involves the recruitment of the thalamic region (fatigue, depression, irritability, food cravings), brainstem (muscle tenderness, neck stiffness), cortex (sensitivity to photo and phono), and limbic response (depression anhedonia).
The prevailing consensus in the migraine community appears to indicate a combination of neuronal and vascular involvement with the trigeminal vascular system (TGVS) complicit in the progression of migraine. Broadly, various triggers initiate migraine to differing degrees, and treatment methodologies target a variety of pathways with varied results; the fundamental mechanism driving change is unclear. In the absence of an identifiable locus for anatomical, biochemical, or pathological change in common clinical migraine, a fundamental question remains unanswered: What endogenous media and pathways link the stimulus to perception of migraine and potentially pain?
The goal of this talk is to highlight progress made in the characterization of acute triggered migraine. To elucidate this neurovascular coupled system, two fundamental mechanisms complicit in neuronal disorders are explored, namely ionic fluxes using sodium MRI and metabolic changes by utilizing proton spectroscopy as well as ongoing efforts to characterize cerebral perfusion—with and without pharmaceutical prophylaxis.
Date: June 26, 2019, at noon
PhD Student and NSF Graduate Fellow
3D Vision Lab
Princeton University
Automatic delineation and measurement of main organs is one of the critical steps for assessment of disease, planning, and postoperative or treatment follow-up. Internal human anatomy is composed of complex shapes that exhibit a large degree of variation, which is challenging to capture using existing modeling tools. We observe that complex shapes can be learned by neural networks from large amounts of examples and summarized using a coarsely defined structure, which is consistent and robust across a variety of observations. Further, shape structure can be used in the synthesis process to improve the quality of generated shapes. We study medical applications of 3D organ reconstruction from topograms and synthetic X-ray prediction and propose several ways of incorporating structure into the synthesis process. We also show compelling quantitative results on 3D liver shape reconstruction and volume estimation on 2129 CT scans.
Date: June 11, 2019, at noon
Weill Cornell Medicine
MRI Research Institute
Ultra High-Field MRI
Magnetic Resonance Imaging (MRI) has emerged as one of the most powerful and informative diagnostic tools in modern medicine. While most clinical MR studies use magnetic field strengths of 1.5T or 3T, leading research is pushing these magnetic field strengths to 7T and beyond. These new ultra high‐field (UHF) technologies promise images with higher spatial resolution, higher sensitivity to subtle change, and novel contrasts, which will in turn improve our basic understanding of anatomy and physiology in both healthy tissue and disease. However, there are substantial hurdles to surmount before we will reap the promised benefits of UHF MRI in clinical applications. This talk will introduce some of the major challenges faced in UHF MRI and will summarize a number of concepts in engineering and multiphysics that are being researched to overcome these issues.
Date: May 22, 2019, at noon
Associate Professor
F.M. Kirby Research Center for Functional Brain Imaging
Kennedy Krieger Institute
Johns Hopkins University
Chronic Kidney Disease (CKD) is a cardinal feature of methylmalonic acidemia (MMA), a prototypic organic acidemia. Impaired growth, low activity, and protein restriction affect muscle mass and lower serum creatinine concentrations, which can delay the diagnosis and management of renal disease in this patient population. We have designed a general alternative strategy for monitoring renal function based on administration of pH sensitive MRI contrast agents to acquire functional information. We have tested our methods in a mouse model of MMA and detected robust differences in the perfusion fraction and pH maps we produce between groups with severe, mild, and no renal disease. Our results demonstrate that MRI contrast agents can be used for early detection and monitoring of CKD, particularly in disorders that alter renal pH and perfusion such as MMA.
Date: May 21, 2019, at noon
Research Assistant Professor
Department of Electrical and Electronic Engineering
The University of Hong Kong
One grand challenge for the 21st century is to achieve an integrated understanding of brain circuits and networks, particularly the spatiotemporal patterns of neural activity that give rise to functions and behavior. Brains form highly complex circuits where circuit elements communicate using electrical and/or chemical signals. Such communications are typically facilitated through long-range projections that interconnect numerous regions, giving rise to a network-like property in the brain. Despite their importance, the functions of long-range projections remain poorly understood. Here, I will show you our recent developments in deploying multimodal techniques in-vivo on rodents to interrogate multisensory brain networks; leveraging the strengths of optogenetics to enable cell-type specific neuromodulation, functional MRI (fMRI) to visualize brain-wide neural activity, and electrophysiology to explore the neural mechanism(s) that underlie our observations. I will present key findings from our work in the multisensory thalamo-cortical, cortico-cortical, and cortical-subcortical circuits, including the unique dynamic spatiotemporal response properties of multisensory pathways as well as their functional relevance. From this talk, I aim to show you how the utilization of multimodal brain imaging techniques can be vital in our quest to achieving an integrated and systemic understanding of large-scale brain-wide multisensory interactions.
Date: May 9, 2019, at noon
Postdoctoral Researcher
Stanford University
In this talk, I will present techniques to reconstruct 3D dynamic MRI of ~100 GBs from non-gated acquisitions. The problem considered is vastly undetermined and demanding of computation and memory. I will introduce a multi-scale low-rank matrix model to compactly represent dynamic image sequences. This enables compressed storage, which, in combination with a stochastic optimization approach, renders the reconstruction of 100s of GBs of images feasible. The proposed method is applied to dynamic contrast-enhanced MRI and free-breathing lung MRI, with reconstruction resolution of near millimeter spatially and sub-second temporally. The attached animated gif shows a 3D rendered result from this talk. (Joint work with Xucheng Zhu, Joseph Cheng, Peder Larson, Shreyas Vasanawala, and Michael Lustig)
Date: May 3, 2019, at noon
Associate Professor
Department of Radiology
Stanford University School of Medicine
Pain is now the #1 clinical problem in the world and, yet, our current imaging methods to correctly identify pain generators remain woefully inaccurate. The fact that meniscal tears, herniated discs, arthritis, and rotator cuff tears are seen in asymptomatic individuals supports the disturbing fact that standard-of-care imaging techniques are extremely poor at pinpointing the exact site of pain generation. This dearth of unreliable diagnostic tools necessarily facilitates significant misdiagnosis, mismanagement, rampant use of opioids, and unhelpful surgeries. Thankfully, relatively recent developments in clinical molecular imaging (MI) are affording the opportunity to pinpoint the exact site(s) of pain generation due to advances in biomarker discovery, imaging technology, and radiotracer design. Our group has developed a highly specific 18F-labeled positron emission tomography (PET) radiotracer for imaging the sigma-1 receptor (S1R), a master regulator of ion channel activity and molecular biomarker of pain generation. Additionally, we have repurposed 18F-fluordeoxyglucose (FDG) as a marker of inflammation by virtue of its proclivity for metabolically active processes. Here, we will describe our experience using these radiotracers in our ongoing PET/MRI clinical trials of patients with chronic pain. Importantly, we will illustrate how this new imaging method is enabling more accurate identification and localization of pain generators and is starting to positively impact the way we treat pain.
Date: May 3, 2019, at 12:30 p.m.
Stanford University School of Medicine
Neuroinflammation is a key pathological feature of many central nervous system (CNS) diseases. Although extensive work in preclinical rodent models demonstrates a significant role for both the innate and adaptive immune response in the initiation and progression of neurological diseases, our understanding of these responses and their contribution to human disease remains very limited. Additionally, both beneficial and toxic inflammatory processes are associated with the progression and remission of neurological disease, and the spatiotemporal course of these complex responses remains a mystery, especially in the clinical setting. Molecular imaging using positron emission tomography (PET) has enormous potential as a translatable technique to enhance our understanding of neuroinflammation in CNS diseases. Our experience with developing new PET radioligands for visualizing the neuroinflammatory component of Alzheimer’s disease, multiple sclerosis, and stroke will be described. I will provide examples regarding our work on designing radioligands for the translocator protein 18 kDa (TSPO), triggering receptor expressed on myeloid cells 1 (TREM1), and two B lymphocyte surface antigens. Specifically, the in vivo role, spatiotemporal dynamics, peripheral contribution, and different functional phenotypes of innate and adaptive immune cells throughout the progression of CNS diseases will be shown. Moreover, I will describe how we are starting to apply these tools to track disease progression, guide therapeutic selection for individual patients, and serve as surrogate endpoints in clinical trials.
Date: April 30, 2019, at noon
Postdoctoral Fellow
NYU Grossman School of Medicine
Although both MRI and CT resolutions are limited, different MRI and X-ray modalities offer possibilities for tissue microstructure analyses. Diffusion MRI is sensitive to proton displacement on the micrometer scale, whereas X-ray photons scatter off the sample’s micro- and nano-structure.
Recently, we developed techniques based on X-ray scattering that allow tomographic investigations of the sample’s fiber orientations. In the brain, these techniques also allow quantifying myelin content due to myelin’s repetitive structure.
In this talk, I will give an overview of my work in CBI in the past two years; I will present applications of these techniques to mouse and human CNS, to derive fiber orientations and myelin content in healthy, diseased, and treated tissue, and comparison to diffusion MRI metrics.
Date: April 25, 2019, at noon
Department of Radiology
Institute for Advancement of Clinical and Translational Science
Kyoto University Hospital, Kyoto, Japan
Diffusion MR imaging has become an important clinical imaging modality in breast imaging, for the detection of malignant lesions and metastases, as well as for therapy monitoring. Some studies have shown that pretreatment ADC might be a useful biomarker to predict response to breast cancer therapy. However, non-Gaussian diffusion might potentially extract more microstructural information than the ADC, as with a high degree of diffusion weighting (high b values) one increases the effects of obstacles to free diffusion present in tissues, notably cell membranes. Indeed, the “kurtosis,” which reflects diffusion non-gaussianity, is high in malignant lesions compared to benign lesions.Still, a particularly challenging problem for breast diffusion MRI is the detection of the non-mass enhancing lesions seen on contrast-enhanced MRI, such as with DCIS. High-resolution images using readout-segmented EPI might overcome the low sensitivity of such lesions. On the other hand, tissue perfusion, which is also available from diffusion MRI images (IVIM effect), gives information on the blood fraction, which appears correlated with vessel density. The IVIM fraction is usually high in malignant lesions, but there seems to be a large overlap with benign lesions. Combination of non-Gaussian diffusion and IVIM parameters appears to boost diagnosis accuracy. Still, the results have been sometimes inconsistent in the literature, partly due to differences in study design (choice of b values and acquisition methods, data analysis approaches, differences in patient population), and the standardization of acquisition protocols and processing methods used for quantitative DWI analysis is a very important step for diffusion MR imaging to become a clinically recognized biomarker.
The investigations on the relationship between the IVIM/diffusion parameters and the underlying tissue structure at the microscopic level, as well as changes induced by therapy, must be pursued using animal models, MRI of specimens at ultra-high resolution, and validation with histology. Reliability and reproducibility of diffusion MRI results must also be assessed to facilitate monitoring disease progression or response to therapy in individual patients.
Date: April 25, 2019, at 10:30 a.m.
NeuroSpin, CEA-Saclay Center, Gif-sur-Yvette, France
NIPS, Okazaki, Japan
Human Brain Research Center, Kyoto University, Kyoto, Japan
The understanding of the human brain is one of the main scientific challenges of the 21st century. Unraveling the biological mechanisms of our mental life should help us understand neurological or psychiatric diseases to allow early diagnosis and treatment of patients, with obvious economic counterparts. In this quest for the human brain, neuroimaging, and especially MRI, has become an inescapable pathway because it allows getting maps of brain structure and function in situ, non-invasively, in patients or normal volunteers of any age. MRI allows brain anatomy of individuals to be visualized in 3 dimensions with great detail, as well as networks of brain regions activated by high-order cognitive functions, together with stunning images of the connections between those areas. Still, images remain at a macroscopic scale (millions of brain cells), while invasive techniques in animals and tissues explore very small ensembles of neurons. This large gap must be bridged to understand how the brain works, as interaction and synergy exist between all brain levels. One approach is to rely on diffusion MRI, a concept that has been developed from the mid-1980s based on Einstein’s framework to probe tissue structure at a microscopic scale while images remain at millimeter scale through parametrization or modeling, providing unique information on the functional architecture of tissues. Since then, diffusion MRI has become a pillar of modern clinical imaging. Diffusion MRI has mainly been used to investigate neurological disorders, but is now also rapidly expanding in oncology, to detect, characterize, or even stage malignant lesions, especially for breast or prostate cancer. In the brain, diffusion MRI even allows us to reveal dynamic changes occurring in tissue microstructure intimately linked to the neuronal activation mechanisms. On the other hand, outstanding instruments operating at a field of 11.7 teslas or above are now emerging to boost the spatial and temporal resolution to not only allow us to “better” see inside our brain, confirming or invalidating our current assumptions on how it works, but also to generate new assumptions and elaborate a kind of “Gauge Theory” to help us decode the functioning of our brain.
Date: April 16, 2019, at noon
Postdoctoral Research Fellow
Memorial Sloan Kettering Cancer Center
The Thomas Fuchs Lab
To overcome the lack of automation and long computational times for advanced PET image reconstruction methods, we present a novel encoder-decoder architecture that quickly reconstructs high-quality images directly from PET sinogram data. DeepPET is trained and evaluated on realistic, simulated data, and resulting images have higher quality than conventional techniques, taking a fraction of the time to generate.
Date: April 9, 2019, at noon
PhD Candidate
Biomedical Imaging Program
Sackler Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Sodium (23Na) MRI has shown promise for monitoring neoadjuvant chemotherapy (NACT) response in breast cancer. Unfortunately, due to low sodium content in the body, its low MR sensitivity and short relaxation times in biological tissues, 23Na MRI suffers from intrinsically low signal-to-noise ratio (SNR), which can be up to 20,000 times lower than that of proton. Such low SNR translates into low spatial resolution and long acquisition times. Efforts to alleviate these challenges generally utilize high field systems (≥ 3 T), ultra-short echo time (UTE) acquisition methods, and tailored radiofrequency coils to boost the baseline SNR. Our focus is the coil design aspect. Specifically, we present a dual-tuned multichannel 1H/23Na bilateral breast coil consisting of volume transmit/receive (Tx/Rx) 1H coils, volume 23Na transmit coils and an 8-channel 23Na receive array for 7 T MRI which enabled sodium imaging in vivo with 2.8 mm isotropic nominal resolution (~5 mm real resolution) in 9:36 min. The proposed coil could enable access to even more specific biomarkers of cellular metabolism such as intracellular sodium concentration, and cellular density such as extracellular volume fraction that are still largely unexplored due to the challenges associated with 23Na MRI.
Date: April 8, 2019, at noon
Associate Research Scientist
Yale School of Medicine
Transcranial Ultrasound (TUS) is an emerging field with a vast range of new potential clinical applications. Here, a series of new human scale TUS devices and the novel benchtop strategies used to develop them in the laboratory will be presented. These devices are intended for brain tumor cancer therapy and for treating neurological disorders such as epilepsy, pain, depression, and essential tremor. The presentation will include the latest developments for: 1) A neuronavigation-guided single-element transducer platform for delivering multi-target pulsed low-intensity TUS to human brain. 2) An integrated scalp sensor for simultaneous electroencephalography and acoustic emission detection. 3) A 3D passive acoustic mapping array device compatible with the FDA approved ExAblate 4000 system for localizing microbubble cavitation. Highlights of each technique relevant to current clinical investigations and future directions of each strategy will be discussed.
Date: April 2, 2019, at noon
PhD Candidate
Biomedical Imaging Program
Sackler Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Sodium (23Na) MRI can provide unique metabolic information to study the human body and its afflictions. However, the low intrinsic SNR of sodium MRI limits the resolution of the images to 3-5 mm isotropic and necessitates long acquisition times (~10-20 min). Moreover, the necessity to perform 1H and 23Na acquisitions sequentially prolongs the total scan time, which impedes the widespread adoption of sodium imaging. In this talk, we will present a technique to simultaneously acquire sodium images and multi-parametric proton maps in one single scan.
Date: March 26, 2019, at noon
PhD Candidate
Biomedical Imaging Program
Sackler Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Prior histological post-mortem studies have highlighted gray matter (GM) microstructural abnormalities as a pathological feature of both schizophrenia (SZ) and autism spectrum disorder (ASD). However, these histological studies were limited by small sample sizes and focus on restricted brain areas. In this talk, we present our work examining the feasibility of diffusional kurtosis imaging (DKI) to describe gray matter microstructural abnormalities in SZ and ASD non-invasively and in vivo. DKI is an extension of diffusion tensor imaging that accounts for non-Gaussian water diffusion contributions to the diffusion MRI signal and provides several kurtosis indices that reflect tissue microstructural complexity. The talk will review existing research investigating DKI’s use to describe GM microstructure pathology in several clinical populations and animal disease models, as well as our recent findings showing significant differences in kurtosis intensity and lateralization metrics in SZ and ASD populations.
Date: March 19, 2019, at noon
PhD Candidate
Medical University of South Carolina
Diffusion MRI (dMRI) has the unique ability to study brain microstructure at a resolution much smaller than the MRI voxel itself. The strength of diffusion weighting (i.e., the b-value) strongly impacts what information is contained in the dMRI signal. Since modern scanners have much stronger gradients, high b-value dMRI is becoming more feasible, and its utilization is likely to increase. High b-value acquisitions provide information beyond what is attainable with DTI and have proven useful for fiber tractography and for calculating diffusion measures that have greater biological specificity. This presentation will revisit a high b-value technique known as fiber ball imaging (FBI) but will mostly focus on how it can be used in combination with diffusion kurtosis imaging (DKI) to estimate microstructural parameters, such as compartmental water fractions and diffusion tensors. In addition, FBI provides the opportunity to calculate compartmental transverse relaxation times (T2) while avoiding multi-exponential fitting schemes.
Date: March 14, 2019, at noon
Nanomedicine Science and Technology Center
Department of Mechanical and Industrial Engineering
Northeastern University, Boston, MA
Magnetic Resonance Imaging (MRI) is an invaluable diagnostic tool for imaging the human body, diagnosing and characterizing diseases, and developing new treatments. In this work, we describe two applications of a novel MRI technique, Quantitative Ultra-short Time-to-echo Contrast-Enhanced (QUTE-CE) MRI to brain disease.
In a first application, QUTE-CE is employed to quantify nanoparticle accumulation in tumors, which is of great clinical interest for stratifying cancer patients who may benefit from therapeutic nanoparticles. Using FDA-approved superparamagnetic iron oxide nanoparticle (SPION) ferumoxytol in QUTE-CE MRI, we produce quantitative measurement of contrast and delineate clear, positive-contrast brain/tumor vasculature image in mice and rats. QUTE-CE MRI is shown to improve contrast and contrast efficiency compared to conventional high-resolution T1- and T2-weighted imaging. QUTE-CE is ideally suited for non-invasive visualization and quantification of tumor nanoparticle uptake, and accordingly, it can potentially be used for identifying cancer patients who can respond to treatment with therapeutic nanoparticles.
In a second application, QUTE-CE is employed to characterize traumatic brain injury (TBI). TBI is a prevalent risk of death and disability in young people with about 1.6 million cases reported per year in the US. Some of the most devastating injuries from brain trauma are the rupturing of arteries between the dura and the skull in an epidural hematoma (blood brain barrier disruption), as well as tears in emissary veins, resulting in hemorrhagic contusions seen in subdural hematomas. This accumulation of blood can squeeze and increase pressure on the brain. Here, we introduce a novel application of QUTE-CE to image blood accumulation and detect microbleeds in mild TBI animals. Rats which underwent 3 mild concussions showed significant difference in QUTE-CE MRI measure of ferumoxytol accumulation in extravascular space indicating blood brain barrier damage following TBI. These differences were observed primarily in cortex, hypothalamus, basal ganglia, cerebellum and brainstem. This study demonstrates that QUTE-CE MRI can be used to detect blood brain barrier disruption and microbleeds in mild TBI rats.
Date: March 7, 2019, at noon
German Centre for Cardiovascular Research
University Medical Center Gottingen
No abstract was provided for this talk.
Date: March 5, 2019, at noon
Research Assistant Professor
Department of Radiology
Perelman School of Medicine at University of Pennsylvania
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults with poor prognosis. The standard of care for patients with GBM includes maximal surgical resection and concurrent chemo-radiation therapy followed by 6 to 12 cycles of adjuvant temozolomide (TMZ). Standard therapeutic approaches provide modest improvement in progression-free and overall survival, necessitating the investigation of novel therapies. Recently, FDA approved the use of tumor-treating fields for the treatment of patients with GBM. Additionally, several immunotherapeutic modalities such as chimeric antigen T cell receptors, check-point inhibitors and dendric cell vaccines hold much promise in the future treatment paradigms for these patients. In this presentation, I will discuss the potential roles of 3D-echoplanar spectroscopic imaging, diffusion and perfusion MR imaging techniques in evaluating treatment response in patients with GBM receiving established and novel treatment modalities. As non-invasive identification of patients harboring isocitrate dehydrogenase (IDH) mutant gliomas can have significant clinical implications, I will also present our initial experience on the utility of 2D-correlational spectroscopy in identifying glioma patients with IDH mutation.
Date: February 12, 2019, at noon
PhD Candidate
Biomedical Imaging Program
Sackler Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Diffusion MRI is sensitive to the length scale of tens of microns, which coincides with the scale of microstructure in the human brain tissue. By varying the diffusion time, we can evaluate the brain micro-geometry via time-dependent diffusion measurements and biophysical modeling. To validate our model, we segmented 3-dimensional realistic microstructure of the mouse brain white matter and performed Monte Carlo simulations of the diffusion in segmented axons. This talk will focus on the time dependence either along or perpendicular to white matter axons and corresponding micro-geometries, such as axonal diameter variation.
Date: January 29, 2019, at noon
Professor of Neuroscience
Associate Director of the Center for Biomedical Imaging
MUSC
Charleston, SC
Fiber Ball Imaging (FBI) is a diffusion MRI method that estimates the orientation of axonal fibers in white matter from an inverse Funk transform. This approach avoids the need for numerical fitting to a signal model and for a fiber response function. FBI also yields predictions for certain microstructural parameters, including the fraction anisotropy axonal. When combined with triple diffusion encoding MRI, FBI can also be used to find the intra-axonal diffusivity and the axonal water fraction. This talk will focus on the basic concepts that underlie FBI but will also show data that support its validity and illustrate its application.
Date: January 25, 2019, at noon
International Sales Manager
MOLECUBES
Belgium
Niek Van Overberghe (International Sales Manager @ MOLECUBES) will present on the unique technology at the core of the β-, γ and X-CUBE, preclinical imagers for PET, SPECT and CT. This new generation of in vivo imaging systems makes use of monolithic crystals coupled to solid state siPMs taking imaging one step further, combined with an in vivo CT system that ensures fast and low dose acquisitions. Thanks to this new technology, researchers can now inject lower activities, scan for a shorter time, hereby reducing the stress level on animals, increasing throughput, lowering radiotracer cost, and lowering the dose of the operator. Because of their unique bench top size, the instruments can be used in any lab around the world without needing building modifications. In addition, Niek will present on different applications that highlight the superior capabilities of these bench top modular systems compared to older systems.
Date: December 17, 2020, at 2:00 p.m.
Location: via Webex
PhD Candidate
Functional MRI Lab
University of Michigan
Pulse design and reconstruction are two important topics in MR research for enabling faster imaging. On the pulse design side, selective excitations that confine signals to be within a small ROI instead of the full imaging FOV can promote sampling sparsity in the k-space, as a direct outcome of the change of the corresponding Nyquist sampling rate.
On the reconstruction side, besides improving algorithms’ capability on restoring images from less data, another objective is to reduce the reconstruction time, particularly for dynamic imaging. This talk presents our developments on these two perspectives: The first part introduces a pulse design framework built on our efficient auto-differentiable Bloch simulator. By propagating the derivatives in an automatic way, this tool connects excitation objectives (e.g., accuracy) directly to the pulse waveforms to be designed without approximations such as the small-tip model. It enables us to address excitation losses that are previously not approachable. We apply this tool on outer volume saturated inner volume imaging, which confines imaging signals into an ROI by selectively spoiling spin magnetizations outside.
Date: November 24, 2020, at 10:00 a.m.
Location: via Webex
PhD Candidate
Max-Delbrück-Centrum für Molekulare Medizin
Berlin Ultrahigh Field Facility
Renal tissue hypoxia is considered to be an important factor in the development of numerous acute and chronic kidney diseases. Blood oxygenation sensitized MRI can provide quantitative information about changes in renal blood oxygenation via mapping of T2*. Simultaneous MRI and invasive physiological measurements in rat kidneys demonstrated that changes in renal T2* do not accurately reflect renal tissue oxygenation under pathophysiological conditions. Confounding factors that should be taken into account for the interpretation of renal T2* include renal blood volume fraction and tubular volume fraction. Tubuli represent a unique structural and functional component of renal parenchyma, whose volume fraction may rapidly change, e.g., due to alterations in filtration or tubular outflow.
Diffusion-weighted imaging (DWI) provides a method for in-vivo evaluation of water mobility. In the kidneys intravoxel incoherent water motion may be linked to three different sources: i) renal tissue water diffusion, ii) blood perfusion within intrarenal microvasculature and iii) fluid in the tubules. The latter provides means to probe for changes in the tubular volume fraction. Recognizing this opportunity this presentation examines the feasibility of assessing tubular volume fraction changes using the non-negative least squares (NNLS) analysis of DWI data.
Date: October 29, 2020, at 9:00 a.m.
Location: via Webex
Associate Professor of Radiology
MIR, Mallinckrodt Institute of Radiology
Washington University School of Medicine in St. Louis
No abstract was provided for this talk.
Date: October 28, 2020, at 2:00 p.m.
Location: via Webex
Assistant Professor
Department of Radiology
Massachusetts General Hospital
This talk will explore new ways to use local magnetic field control besides conventional “B0 shimming”. Perturbations of the main magnetic field (“B0”) due to tissue susceptibility interfaces are a long-standing obstacle in Magnetic Resonance applications. Inhomogeneous B0 fields can lead to artifacts such as geometric distortion, signal voids, poor RF pulse performance, and spectral line broadening. This has limited the use of diffusion, functional, and spectroscopic MR imaging in many regions of the brain and body. Recently, it has been shown that multi-coil arrays of independently-driven loops placed close to the body can generate nonlinear, high spatial-order field offsets to “shim out” unwanted susceptibility fields on a subject-specific basis, benefiting field homogeneity and image quality. In this talk, we explore the potential for repurposing multi-coil shim arrays for new applications that exploit their nonlinear, rapidly-switchable local field offsets. Examples include tailored field offsets for improved lipid suppression in MR spectroscopic imaging; zoomed functional MRI of target brain anatomy; flip angle correction at ultra-high field; and supplementary spatial encoding for improved parallel imaging. We will also explore ways to add local field control capability to coil arrays originally designed for other applications, such as RF receive arrays and Transcranial Magnetic Stimulation probe arrays, so that their degrees of freedom can be brought to bear.
Date: October 21, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor
University of British Columbia
The presentation will provide a broad overview of the history of myelin water imaging in humans. Myelin water imaging is based on measurement of the short T2 component of water in brain and spinal cord tissue. What began as a lengthy single slice, single center measurement has expanded to many countries on multiple continents in just over 25 years. Important work along the way has included post-mortem validation studies in human CNS tissue, comprehensive assessment of development and normal characterization in adults, as well application to many neurological diseases including multiple sclerosis, concussion, stroke and beyond. The creation of normative atlases and development of faster analysis approaches promises to help move myelin water imaging to clinic in the coming decade.
Date: September 30, 2020, at 2:00 p.m.
Location: via Webex
PhD Candidate in Translational Neuroscience
Graduate Research Fellow
Sastry Foundation Advanced Imaging Laboratory
Department of Psychiatry and Behavioral Neurosciences
Wayne State University
Detroit, MI
Parkinson disease (PD) is a neurodegenerative disorder characterized pathologically by nigrostriatal dopaminergic terminal loss and the development of Lewy pathology in surviving neurons of the substantia nigra (SN). Lewy pathology extends beyond the SN, and can be found in limbic and prefrontal cortical regions associated with cognitive decline. In vivo assessment of cortical microstructure and the extent of pathological changes will be clinically useful to monitor disease progression. For this purpose, our study used two diffusion MRI models, diffusion tensor imaging and neurite orientation dispersion and density imaging, to study the microstructural changes in the cerebral cortex of PD participants (n=18) compared to healthy controls (n=8). We demonstrate that in the absence of cortical thinning, PD pathology is associated with significant abnormalities in cortical diffusion metrics. Specifically, we found that the anterior cingulate cortex and inferior temporal lobe are consistently involved in PD through reductions in the intracellular volume fraction, fractional anisotropy (FA) and increased orientation dispersion index. FA reductions were extensive and involved more limbic areas such as entorhinal cortex, parahippocampus and insula. These findings are consistent with the presence of Lewy pathology in limbic regions and might be reflecting the earliest stages of tissue involvement in PD.
Date: September 29, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor
Department of Radiology
University of Michigan
Cardiovascular Magnetic Resonance (CMR) is a valuable tool that enables non-invasive characterization of tissue and assessment of cardiac function. Parametric mapping techniques play an important role in CMR due to their sensitivity to physiological and pathological changes in the myocardium. The capability of mapping T1 and T2 simultaneously in a single scan makes the novel cardiac Magnetic Resonance Fingerprinting (cMRF) technique a promising technology to facilitate diagnosis and treatment evaluation in various cardiac diseases. Unlike conventional parametric mapping approaches which may yield different T1 or T2 values for the same subject depending on the specifics of the MRI system hardware or pulse sequence implementation, cMRF has the potential to offer reproducible measurements of tissue properties on all MRI scanners. This talk aims to introduce the basics of the cMRF technique, including pulse sequence design, dictionary generation, and pattern matching, as well as highlighting potential applications.
Date: September 22, 2020, at 2:00 p.m.
Location: via Webex
Professor, Director C.J. Gorter Center for High Field MRI
Department of Radiology
Leiden University Medical Center
Commercial magnetic resonance imaging (MRI) systems cost millions of euros to purchase, require large electromagnetically shielded spaces to house, are extremely expensive to maintain and require highly trained technicians to operate. These factors together means that their distribution is confined to centrally-located medical centres in large towns and cities. Globally over 70% of the world’s population has absolutely no access to MRI, and clinical conditions which could benefit from even very simple scans cannot be treated. In the financially developed world, although MRI is diagnostically very important, the high cost and fixed nature prohibits any type of role in widespread health screening, for example. The magnetic fields typically used are very high, which means that there are severe contraindications so that, for example, MRI cannot currently be used in the emergency room. From the considerations above it is clear that if low-field MRI could be made more portable, accessible and sustainable then it would open up new opportunities in both developed and developing countries.
Rather than designing a highly sophisticated and expensive piece of equipment that can be used for all types of scanning, we use the philosophy of tailored design, such that we can design much more inexpensive systems for specific medical applications. Thus rather than one large MRI, the model is similar to having tens of different mobile ultrasound machines in a medical facility. In order to achieve portability, we design systems that use thousands of very small low-cost permanent magnets, arranged in designs which have no fringe field and therefore very easy siting requirements. The low magnetic fields allow scanning of patients with implants, and the scanner could potentially be transported on an ambulance for differentiation of hemorrhagic or ischemic stroke, for example. This talk will cover aspects of magnet, gradient and RF coil design for low fields (~50 mT), as well as corrections for gradient- and B0-distortions, and present the latest in vivo results as well as an outlook on future developments.
Date: August 5, 2020, at 2:00 p.m.
Location: via Webex
Assistant Professor/Emerging Scholar of Electrical and Computer Engineering
Engineering Division
NYU Abu Dhabi
There is a pressing need to identify deterioration amongst patients with COVID-19 in order to avoid life-threatening adverse events. Chest radiographs are frequently collected from patients presenting with COVID-19 upon arrival to the emergency department, since it is considered as a first-line triage tool and the disease primarily manifests as a respiratory illness. In this talk, I will discuss the AI prognosis system we developed using data collected at NYU Langone Health to predict in-hospital deterioration, defined as the occurrence of intubation, mortality, or ICU admission. In particular, our system consists of an ensemble of an interpretable deep learning model to learn from chest X-ray images and a gradient boosting model to learn from routinely collected clinical variables, e.g. vital signs and laboratory tests. The system also computes deterioration risk curves to summarise how the risk is expected to evolve over time. The results of retrospective validation on the held-out test set, the reader study, and silent deployment in the hospital infrastructure highlight the promise of our AI system in assisting front-line workers through real-time assessment of prognosis.
Date: July 22, 2020, at 2:00 p.m.
Location: via Webex
Assistant Professor
Athinoula A. Martinos Center for Biomedical Imaging
Department of Radiology
Massachusetts General Hospital, Harvard Medical School
Less is known about the structure-function relationship in the human brain than in any other organ system. The challenge of studying brain structure is that brain networks span multiple spatial scales, from individual neurons all the way to whole-brain systems. Diffusion magnetic resonance imaging (MRI) holds great promise among noninvasive imaging methods for probing cellular structure of any depth and location in the living human brain. Robust methods for in vivo mapping of tissue microstructure by diffusion MRI remain elusive due to the demand for fast and strong diffusion-encoding gradients. I will present an overview of our group’s efforts to advance MR hardware, biophysical modeling, and validation of microstructural metrics derived from diffusion MRI in order to probe the structure of the human brain across multiple scales. I will review current progress and applications of these methods to study axonal microstructure in the normal and aging human brain and assess axonal damage in multiple sclerosis.
To bridge the divide between the neuroscientific and clinical use of MRI in probing tissue microstructure, this presentation will also provide an overview of our ongoing efforts to optimize, translate and validate novel encoding and reconstruction techniques for the ultrafast acquisition of high-resolution, multi-contrast MR images in a clinical setting. These efforts are exemplified in our recent work exploring the benefits of improved speed and resolution of ultrafast susceptibility-weighted imaging to study microvascular injury in patients with severe COVID-19 using radiologic-pathologic correlative examinations.
Date: July 15, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor
Radiology Department and Advanced Imaging Research Center
UT Southwestern Medical Center
Recently, methods employing single- and dual-frequency saturation are gaining recognition to detect events on microstructural and molecular level. Specifically, Chemical Exchange Saturation Transfer (CEST) employs selective saturation of the exchanging protons and subsequent detection of the water signal decrease to create images that are weighted by the presence of a metabolite or pH. Here, we will describe aspects of translating CEST to reliable clinical applications at 3 Tesla and discuss its potential uses in human oncology, specifically breast cancer. Second, we will discuss a method called inhomogeneous Magnetization Transfer (ihMT), which employs dual-frequency saturation to create contrast originating from the residual dipolar couplings and thus specific to microstructure. We will focus on principles of ihMT, its comparison to other white matter metrics (diffusion) and the methods application to the detection of myelin in brain and spinal cord.
Date: May 27, 2020, at 11:00 a.m.
Location: via Webex
Senior Research Scientist
High-Field MR Center
Max Planck Institute for Biological Cybernetics
Tubingen, Germany
Due to a substantial shortening of the RF wave length (below 15 cm at 7T), RF magnetic field at UHF has a specific transmit (Tx) excitation pattern with strongly decreased (more than 2 times) values at the periphery of a human head. This effect is seen not only in the transversal slice but also in the coronal and sagittal slices, which considerably limits the longitudinal Tx-coverage (along the magnet’s axis) of conventional surface loop head arrays. In this work, we developed a novel human head UHF array consisted of 8 transceiver folded-end dipole antennas circumscribing a head. Due to the asymmetrical shape of the dipoles (bending and folding) and the presence of an RF shield near the folded portion, the array simultaneously excites two modes, i.e. a circular polarized mode of the array itself, and the TE mode (“dielectric resonance”) of the human head. Mode mixing can be easily controlled by changing the length of the folded portion. Due to this mixing, the new dipole array improves longitudinal coverage as compared to unfolded dipoles. By optimizing the length of the folded portion, we can also minimize the peak local SAR value and decouple adjacent dipole elements.
Date: May 20, 2020, at 2:00 p.m.
Location: via Webex
Associate Dean for Mentoring and Professional Development
Professor and Senior Vice Chair of Radiology
Vice Chair of Academic Affairs
Director, Clinical Faculty Mentoring
NYU Langone Health
This lecture will provide a brief clinical overview of SARS-CoV-2 infection and COVID-19 manifestations in the lungs. Imaging findings in the chest will be defined and literature reports summarized. Our evolving clinical experience will be described, including the subacute and chronic manifestations of COVID-19 lung disease we are now seeing. Finally, completed and ongoing thoracic COVID research projects will be presented.
Date: May 13, 2020, at 2:00 p.m.
Location: via Webex
Director, Microstructure Imaging Lab
Assistant Professor of Radiology – Division of Neuroradiology
University of Iowa
Magnetic Resonance Imaging has revolutionized the field of neuroscience by providing a non-invasive means to study the brain, to understand its organization, specialization, and anomalies in an unprecedented manner. Despite the rapid advances in MRI instrumentation, it is still challenging to achieve high-quality data in an efficient manner for several MR imaging modalities, especially for those modalities involving multi-dimensional imaging. In this talk, I will discuss several computational approaches that we have developed to achieve high efficiency MR imaging to enable many applications. These approaches strive to achieve high resolution, high SNR, and artifact-free MRI by jointly optimizing the contribution of MR acquisition, the signal modeling under investigation, and the reconstruction methods to provide meaningful information in an efficient manner. In this talk, I will focus the discussion mainly on diffusion magnetic resonance imaging and our work towards improving the efficiency of this modality.
Merry Mani received her PhD in 2014 from the University of Rochester, NY. Later in 2014, she joined the Magnetic Resonance Research Facility at the University of Iowa as a post-doctoral research fellow, where she developed new imaging methods on the 7T MRI. In 2019, she became an Assistant Professor in the department of Radiology, Carver College of Medicine, University of Iowa. Her lab focuses on integrating cross-disciplinary tools such as signal modeling and signal processing with imaging physics and image analysis tools to enable high efficiency MRI. These include the development of novel pulse sequences and optimization of sampling trajectories and reconstruction methods for maximum performance.
Date: May 6, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor
Radiology and Biomedical Imaging
Yale University School of Medicine
Like standard gradients, nonlinear gradients modulate the magnitude of Bz as a function of position; the difference is that the magnitude as a function of position is generally not linear or unidirectional. One important consequence of gradient nonlinearity is that the modulation of spins is no longer sinusoidal, so MR data do not correspond to points in k-space. Therefore, early encoding strategies focused on optimizing sequences by considering encoding in the spatial domain. However, a k-space analysis of nonlinear encoding provides significant insights on sequence design and suggests novel strategies, such as FRONSAC encoding. With FRONSAC, most of the encoding comes from a standard linear trajectory (e.g. Cartesian, radial or spiral), but nonlinear gradients are used to effectively increase the width of the k-space trajectory. For an undersampled scan, the additional width reduces gaps in k-space and improves reconstructions, but most other properties of the underlying linear method are unchanged. For example, Cartesian-FRONSAC retains features like insensitivity to off-resonance spins and timing delays, ease of changing FOV, resolution, and orientation, and relatively simple contrast behavior, while still allowing for higher undersampling factors. This versatile approach can be added to nearly any sequence, improving undersampling artifacts even for low channel arrays, as we have shown by acquiring a full FRONSAC-enhanced brain protocol in a cohort of healthy subjects.
An additional emerging application of nonlinear gradients is in generating diffusion contrast. In some sense, a linear gradient is the maximally egalitarian way to distribute a ΔB(x): it generates the same Gx (d(ΔB)/dx) everywhere, but the peak Gx across the FOV is the lowest possible. By allowing nonlinearity, Gx is different at each voxel, but it can be concentrated to certain regions of interest. Thus, for specialized applications, it may be possible to achieve massive gradient strengths and very high diffusion weightings using simple equipment. For example, for prostate DWI, we propose an inside-out nonlinear gradient, which simulations suggest will ultimately double CNR in ADC maps.
Date: April 29, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor and Associate Chair of Graduate Education
Department of Biomedical Engineering
University of Michigan
Wouldn’t it be great to perform a surgery without incision or bleeding? “Histotripsy” is the first non-invasive, non-ionizing, and non-thermal ablation technique that is invented by Dr. Xu and her colleagues at the University of Michigan. Using ultrasound pulses applied from outside the body and focused to the target diseased tissue, histotripsy produces a cluster of energetic microbubbles at the target tissue using the endogenous gas pockets with millimeter accuracy. These microbubbles, each similar in size to individual cells, function as “mini-scalpels” to mechanically fractionate cells to acellular debris in the target tissue. The acellular debris is absorbed over time via metabolism, resulting in effective tissue removal. Off-target tissue remains undamaged and no incision is needed. Thus histotripsy can perform non-invasive surgery guided by real-time imaging. Histotripsy has potential for many clinical applications where non-invasive tissue removal is desired. Recent research in Dr. Xu’s lab also shows potent immune response and abscopal effects induced by histotripsy and its potential for immunotherapy. Dr. Xu will talk about the mechanism and instrumentation development of histotripsy as well as the latest pre-clinical and clinical studies of histotripsy for cancer, neurological, cardiovascular, and immunotherapy applications.
Zhen Xu is a tenured Associate Professor and Associate Chair of Graduate Education at the Department of Biomedical Engineering at the University of Michigan, Ann Arbor, MI. She received the Ph.D. degree in biomedical engineering from the University of Michigan in 2005. Her research focuses on ultrasound therapy and imaging, particularly histotripsy. She received the IEEE Ultrasonics, Ferroelectrics, and Frequency Control (UFFC) Outstanding Paper Award in 2006; National Institute of Health (NIH) New Investigator Award at the First National Institute of Biomedical Imaging and Bioengineering (NIBIB) Edward C. Nagy New Investigator Symposium in 2011, The Federic Lizzi Early Career Award from The International Society of Therapeutic Ultrasound (ISTU) in 2015, the Fellow of American Institute of Medicine and Bioengineering in 2019, and The Lockhart Memorial Prize for Cancer Research in 2020. She is an associate editor for IEEE Transactions on UFFC and Frontiers in Bioengineering and Biotechnology, Deputy VP of UFFC Ultrasonics Standing Committee, and an elected board member of ISTU. She is a principal investigator of grants funded by NIH, Office of Navy Research, American Cancer Association, and Focused Ultrasound Foundation. She is also co-founder of HistoSonics, a startup company developing histotripsy for oncological applications.
Date: April 22, 2020, at 2:00 p.m.
Location: via Webex
Principal Investigator
Center for Research in Computer Vision (CRCV)
University of Central Florida
Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this talk, I will share our unique experience for developing a paradigm shifting computer aided diagnosis (CAD) system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. In other words, we are creating artificial intelligence (AI) tools that get benefits from human cognition and improve over complementary powers of AI and human intelligence. We first developed an eye-tracking interface providing radiologists with a real radiology reading room experience. Second, we proposed a novel computer algorithm that unifies eye-tracking data and a CAD system. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve their diagnostic decisions. The proposed C-CAD system has been tested in a lung and prostate cancer screening experiment with multiple radiologists. More recently, we also experimented brain tumor segmentation with the proposed technology leading to promising results. In the last part of my talk, I will describe how to develop AI algorithms which are trusted by clinicians, namely “explainable AI algorithms”. By embedding explainability into black box nature of deep learning algorithms, it will be possible to deploy AI tools into clinical workflow, and leading into more intelligent and less artificial algorithms available in radiology rooms.
Date: April 15, 2020, at 2:00 p.m.
Location: via Webex
Associate Professor, Harvard Medical School
Associate Investigator, Massachusetts General Hospital
Athinoula A. Martinos Center for Biomedical Imaging
This talk will provide an overview of work that our group has done on mapping connectional anatomy from diffusion MRI, and a preview of where this path might lead us next. First, I will discuss our previously developed algorithms for reconstructing white-matter pathways from diffusion MRI. These include both supervised and unsupervised methods with a common theme: like neuroanatomists, they define white-matter bundles based on relative position with respect to neighboring anatomical structures, rather than based on absolute coordinates in a template space. This makes them robust to individual variability and to the effects of disease or healthy development and aging.
Second, I will present results from recent post mortem validation studies, where we have evaluated the accuracy of diffusion MRI with respect to polarization-sensitive optical coherence tomography in human samples, or chemical tracing in non-human primates. Our results suggest that existing methods for inferring the orientation of axon bundles from diffusion MRI do not benefit substantially from very high b-values. This implies that our analysis tools have not kept up with the rapid progress of our hardware, and that new tools are needed to fully take advantage of the data that can be acquired by today’s ultra-high-gradient MRI scanners. I will end the talk by discussing how we may be able to address this, by using the post mortem data not only to evaluate existing methods but to engineer the next generation of tractography algorithms.
Date: April 8, 2020, at 2:00 p.m.
Location: via Webex
PhD Student
Biomedical Imaging and Technology Program
Sackler Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Myelin abnormalities in schizophrenia spectrum disorders have been suggested by histological studies, which have shown aberrations in myelin lamellae, oligodendrocyte structure, and myelin- and oligodendrocyte-related gene expression. However, in vivo examination of myelin content, especially the intra-cortical myeloarchitecture remains limited. In our current project, we employ magnetization transfer imaging to derive macromolecular proton fraction (MPF), a quantitative estimate of myelin content. This talk will focus on data suggesting a flattening of the cortical myelin profile in patients with schizophrenia spectrum disorders and an association of cortical myelin alterations with illness progression and cognitive outcomes. Preliminary findings on whole-brain myeloarchitectural similarity changes in schizophrenia will also be presented.
Yu Veronica Sui is a second-year graduate student in Sackler Institute’s Biomedical Imaging and Technology training program working with Mariana Lazar. She has a background in cognitive psychology and is interested in developing and employing new imaging and analytics methods to characterize the neural bases of psychiatric disorders. Her focus in Lazar Lab is psychosis-related pathological changes in the brain, including both microstructural and connectivity abnormalities.
Date: February 11, 2020, at noon
PhD Student
Biomedical Imaging and Technology PhD Training Program
Sackler Institute of Graduate Biomedical Sciences
NYU Langone Health
No abstract was provided for this talk.
Date: February 7, 2020, at noon
Director, MRI Program
National Heart, Lung, and Blood Institute (National Institutes of Health)
Lower field strength MRI systems paired with high-performance hardware and advanced imaging methods offer unique opportunities for clinical imaging. Specifically, this system configuration offers improved safety for MRI-guided invasive procedures, improved imaging in high-susceptibility regions including the lung, and advantages for efficient image acquisitions. In light of developments in MRI engineering and available computational power, and as well as the drive to reduce MRI costs, there is significant value in revisiting lower field MRI in the context of modern clinical imaging. This talk will describe the experience of the NHLBI imaging patients on a ramped down 0.55T system for 2 years.
Dr. Adrienne Campbell-Washburn is the Director of the MRI Technology Program at the National Heart, Lung, and Blood Institute (National Institutes of Health). Her research focuses on the development of MRI technology for cardiac imaging, lung imaging, and MRI-guided interventions. She works on developing advanced MRI acquisitions that leverage non-Cartesian sampling and reconstruction methods using state-of-the-art computational resources in the clinical environment. Her research aims to improve SNR-efficiency, imaging speed, interventional procedural guidance including device safety and visibility, motion robustness, quantification, and clinical integration.
Date: January 28, 2020, at noon
Professor
Wayne State University School of Medicine
Imaging biomarkers that bridge neuronal abnormalities in vivo and behavior, and animal models and human patients, are urgently needed to quicken the discovery and application of novel disease-modifying therapy, but are not yet available. I will be discussing novel MRI and OCT approaches for measuring sustained and excessive production of free radicals (i.e., oxidative stress) in neuronal laminae without a contrast agent in untreatable neurodegenerative disease. These studies set the stage for translating and managing anti-oxidant treatment in patients for the first time.
Date: January 21, 2020, at noon
Postdoctoral Fellow
Stanford University
Existing clinical infrastructures severely under-utilize modern computation resources, leading to costly errors, slow workflows, and limited research opportunities. However, trends in cloud computing and machine learning are rapidly changing this landscape. Tech companies, such as Amazon, Google, and Microsoft, are now racing to integrate high-performance computing into clinical settings. Medical imaging stands to gain tremendously from advances in computing power, which will enable many previously unthinkable applications.
In this talk, I will focus on three directions on leveraging these emerging computing resources to improve medical imaging: 1) reconstructions of high-dimensional volumetric dynamic MRI on the order of 100GBs; 2) continuous learning and image quality improvement from undersampled datasets; 3) optimizing end-to-end systems across clinical workflow.
Date: January 14, 2020, at noon
PhD Student, Biomedical Imaging and Technology
Sackler Institute of Graduate Biomedical Sciences
NYU Grossman School of Medicine
Dynamic MR image quality is limited by the temporal and spatial resolution trade-off. Adopting machine learning in the reconstruction network provides an alternative method to reconstruct the image of better quality. This presentation will focus on our work using Recurrent Neural Networks (RNN) as a regularizer in our dynamic MR reconstruction network. The regularizer is designed to take time series of k-space with flexible lengths and use it to reconstruct image series. I will show how we design simulations to get ground truth images for model training. Then I will show the output of the trained network on simulated breast perfusion MR data with a comparison to other reconstruction methods.
Zhengnan Huang joined Florian Knoll’s lab in 2018. He has an educational background in bioinformatics. His research interest is MR image reconstruction and machine learning applications.
Date: December 21, 2017, at noon
School of Electrical and Computer Engineering and Stephenson Cancer Center
University of Oklahoma
Developing precision medicine requires accurate prediction markers and/or models to identify the personalized disease (e.g., cancer) risk and prognosis or response to the different treatment. Radiographic medical imaging is widely used in clinical practice and carries much useful information to phenotype disease risk and prognosis. However, how to reliably and quantitatively extract and compute the useful image features, which can be used to develop new and highly performed clinical prediction models remain a very challenged and hot research topic in the biomedical imaging and informatics field. In this presentation, I will discuss the general concept of applying the quantitative image feature analysis in this research field and report several research work recently conducted in our laboratory to identify new quantitative imaging markers and apply machine learning technology to develop new prediction models, which include (1) using a new imaging marker based on the bilateral mammographic density asymmetry computed from the negative mammograms to predict risk of cancer detection in the next subsequent mammography screening; (2) extracting image features from breast MR images to predict complete response (CR) of breast tumors to the neoadjuvant chemotherapy; (3) using tumor density heterogeneity features computed from lung CT images to build a prediction model to assess lung cancer recurrence risk after surgery; and (4) using image features computed from abdominal CT images to predict response of ovarian cancer patients to chemotherapy at the early stage of the clinical trials.
Date: December 19, 2017, at noon
Professor, Director C.J.Gorter Center for High Field MRI
Leiden University Medical Center
This talk will describe recent developments in several areas of magnetic resonance hardware and sequences which have been applied to clinical research and patient scanning at field strengths between 1.5 and 7 Tesla. Topics will include the design of very high permittivity materials/metamaterials for improved magnetic field homogeneity and lower power deposition, new ceramic-based resonators for multi-element transmit arrays, methods for the rapid non-invasive estimation of tissue conductivity, high resolution motion-free imaging of the eye, and whole-body optical-based measurement of temperature changes. Clinical applications include studies of patients with eye tumours, epilepsy, early-onset Alzheimers as well as muscular and neuromuscular dystrophies.
Date: December 15, 2017, at noon
Professor at Cold Spring Harbor Laboratory
Cold Spring Harbor, NY
In 2016, it is estimated that internet IP traffic reached 1021 bits – within striking distance of the Avogadro number. Given that data sizes are reaching thermodynamic proportions, and that relevant calculations have often to be performed in a distributed manner, it can be expected that phenomena and methods from the statistical physics of many particle systems are relevant.
This talk will examine a couple of examples where phase-transition like phenomena occur, with network performance going from a “good” to a “bad” phase sharply as a function of a relevant global parameter. The examples include the so called network consensus problem, and feature selection in multivariate regression using an L1 norm.
Date: December 14, 2017, at 11:00 a.m.
PhD Candidate
University of Michigan, Ann Arbor
In quantitative MRI (QMRI), one seeks to accurately and rapidly localize biomarkers (i.e., measurable tissue properties) using MR data. One key challenge of QMRI is that ‘accurate’ and ‘rapid’ are often competing goals: more physically accurate MR signal models typically depend on more biomarkers, but estimating more markers usually requires longer acquisitions and greater computation. In this talk, I will discuss two recently developed methods to systematically limit these QMRI resource burdens. First, I will describe a method to assemble fast, statistically informative acquisitions that enable min-max optimally precise biomarker estimation. Second, I will describe a machine-learning inspired method to “learn” an extremely fast and scalable biomarker estimator from purely simulated training data. Finally, I will describe our ongoing efforts to apply these methods for fast, accurate myelin water fraction imaging. This talk discusses joint works with Prof. Jeffrey Fessler, Dr. Jon-Fredrik Nielsen, and Prof. Clayton Scott, all at the University of Michigan.
Date: December 12, 2017, at noon
PhD Candidate in Electrical Engineering
Stanford University
Compressed sensing (CS) MRI enables fast imaging. Conventional CS MRI reconstruction algorithms are time-consuming and often lead in undesired over-smoothing or artifacts. Recently, various methods have been proposed to apply deep learning models for more efficient and accurate MRI reconstruction. However, there are still open question on how to ensure realistic and consistent Deep Reconstruction. In this talk, a MRI reconstruction technique using deep learning and generative adversarial network (GAN) is introduced. Evaluated on clinical MRI datasets with both quantitative metrics and radiologists’ ratings, the proposed method demonstrates superior performance compared with conventional iterative reconstruction and Deep Learning models trained with pixel-wise loss. Similar deep learning models can also be applied for PET reconstruction and quantitative MRI.
Date: December 6, 2017, at noon
Universität Stuttgart, Germany
No abstract was provided for this talk.
Date: December 5, 2017, at noon
Chief Research Coordinator and Technical Specialist
Random Walk Imaging (RWI)
Evidence that conventional (linear) diffusion encoding is not enough to probe all relevant features of microstructure has accumulated for 20 years. Recent developments have seen the canonical Stejskal-Tanner experiment complemented with techniques that all contribute more specific information about the underlying structure. The lecture will survey several methods based on diffusion encoding with non-conventional gradient waveforms, and what microstructural features that they can resolve.
Date: November 14, 2017, at 12:30 p.m.
Senior Research Scientist
Siemens Healthcare Technology Center
Medical Imaging Technologies
Siemens Medical Solutions USA, Inc.
Siemens Healthineers
Princeton, New Jersey
Technology leaders have recently announced the goal of translating thoughts into text directly from brain recordings. Existing work on decoding linguistic meaning from imaging data has been largely limited to concrete nouns, and trained and tested with similar stimuli from a few semantic categories. I will present a new approach for building a brain decoding system, based on a procedure for broadly sampling a semantic space constructed from massive text corpora. By efficiently selecting training stimuli shown to subjects, we ensure the ability to generalize to new meanings from limited imaging data. To validate this approach, we trained the system on imaging data of individual concepts, and showed it can decode imaging data of sentences from a wide variety of concrete and abstract topics in two separate datasets.
Date: November 14, 2017, at noon
Siemens Healthcare Technology Center
Medical Imaging Technologies
Siemens Medical Solutions USA, Inc.
Siemens Healthineers
Princeton, New Jersey
Brief overview of the current research activities at Siemens Healthcare Technology Center, Medical Imaging Technologies. Located in Princeton, NJ, we are the central research and development lab of Siemens Healthineers. Our team of over 80 research scientists and software engineers specializes in using large collections of data to build artificial intelligence solutions for healthcare. We also work closely with Siemens’ customers in submitting grant proposals to government funding agencies. Our research has resulted in multiple scientific contributions in the fields of medical imaging, modeling, and image-guided therapy and has been incorporated into many clinical products.
Date: November 10, 2017, at noon
University of Pittsburgh
The advancement of diffusion MR imaging (dMRI) acquisition, post-processing, and clinical diagnostic precision would be accelerated with a cross-laboratory anisotropic diffusion phantom providing paramedic control of shape geometry, packing density and routing. Our group is developing such a phantom matched to histology geometry on a 1 to 1 scale. We have created idealized axons (iAxons) that are textile-based hollow fibers at nanometer scale. They provide controlled geometrical configurations and packing density patterns. The iAxons have a diameter range from 0.2 to 36 microns filled with water covering and exceeding the biological range allowing parametric tests of dMRI precision. We create Standard iAxon Fasciculi (SIF) that contains 950-nanometer internal diameter water filled tubes with a density of a million per mm2. We can create cortical networks such as the eye to LGN of millions of iAxons with precise 50 micron routing positional control. We use non-MRI measurement with Micro CT, light, and electron microscope imaging of iAxons to quantify dMRI precision. We are creating matched histology and phantoms for pig harvested and human cadaver tissue. We are testing bio-physical models like NODDI or spherical mean techniques (SMT) for packing density pattern and amount of iAxons. We have found the intra-cellular volume fraction correlates with a number of iAxons (r = 0.96). For geometrical configuration, we have tested Constrained Spherical Deconvolution techniques which show promising results to resolve more than 45-degree crossing. We will also present the effect of small/big delta on diffusivities at multiple packing densities of the iAxon bundle. We plan to provide phantoms across laboratories and release public data sets to drive MRI-based quantitative calibration and discovery of improved techniques. We have done cross instrument measurement and found large systematic errors in measurement (35%) across instruments at five sites. We are developing correction methods for clinical scanners. We expect the phantoms to provide a set of ground truth challenges to advance MRI diffusion physics and tractography.
Date: October 31, 2017, at noon
Science and Technology Explorer
Focused Ultrasound Foundation
Focused Ultrasound is a novel treatment modality that displaces (minimally) invasive surgery with a totally non-invasive approach using a focused beam of ultrasound energy. Depending on the parameters used, the effect at the focal point can be purely mechanical, thermal or a combination thereof. Coupled with real-time feedback of MRI enables to accomplish a spatio-thermal closed-loop procedure, which may lend itself to automation.
In my talk I will review the history of MRgFUS, the current clinical indications it is being used for and some new emerging applications. I will also describe the role of the Focused Ultrasound Foundation, a non-profit aimed at accelerating clinical adoption, in how NYU may benefit from research grants provided by the Foundation.
Date: October 27, 2017, at 9:00 a.m.
Director, The Helen and Martin Kimmel Institute in Magnetic Resonance
The Bertha and Isadore Gudelsky Professorial Chair
Head, Department of Chemical and Biological Physics
Weizmann Institute, Israel
No abstract was provided for this talk.
Date: December 17, 2017, at noon
Associate Professor
University of Western Ontario, Canada
Research Associate
Lawson Health Research Institute
No abstract was provided for this talk.
Date: October 16, 2017, at noon
Postdoctoral Fellow
Weizmann Institute of Science, Israel
No abstract was provided for this talk.
Date: October 13, 2017, at noon
University College London
Quantitative Magnetic Resonance Imaging (qMRI) enables the non-invasive measurement of microstructural properties of living tissue, thus providing useful imaging biomarkers with strong clinical potential. In practice, while qMRI is rather popular and successful in the brain, qMRI of the spinal cord is more difficult due its proneness to noise, field inhomogeneity and physiological artifacts, which hamper the clinical translation of most qMRI methods. In this talk, I will provide an overview of spinal cord qMRI and illustrate its challenges and report on recent developments. In particular, the talk will focus on recent spinal cord qMRI approaches for neuronal morphology and myelin measurement, which hold promise for more accurate diagnosis and prognosis in conditions such as multiple sclerosis.
Date: October 10, 2017, at noon
Assistant Professor of Biomedical Engineering
Department of Radiology, Weill Cornell Medicine, New York, NY
Positron Emission Tomography (PET) has been nowadays established as a molecular imaging modality capable of providing non-invasive, diagnostic and treatment response assessments of the activity of specific molecular processes underlying a spectrum of oncologic, cardiovascular and neurologic diseases. In the first part of this talk we will introduce a clinically adoptable WB dynamic 18F-FDG PET/CT scan protocol coupled with a family of robust direct 4D PET image reconstruction methods to enable for the first time WB multi-parametric PET imaging in humans. The presented framework exploits current state-of-the-art clinical PET systems technologies, such as Time-of-Flight and Resolution modeling, to also support combined WB static and parametric PET imaging from only the standard-of-care scan time window to deliver to clinic additional and highly quantitative information content beyond the standardized uptake value (SUV) metric. Later in the talk, we will also present a novel dual-tracer 18F-FDG:18F-NaF PET/MR imaging framework designed to improve PET attenuation correction in PET/MR studies by robustly segmenting the bone tissues from the 18F-NaF kinetic analysis. Finally, we will demonstrate a clinically adoptable dual-tracer dual-modality imaging protocol for the simultaneous and co-registered anatomical and molecular assessment of both inflammation and micro-calcification, two major molecular mechanisms considered to be associated with atherosclerosis, in human carotid vessel walls.
Date: October 3, 2017, at noon
Vice Chair for Research, Department of Radiology
Professor of Radiology and Medicine (Cardiology)
Director, Translational and Molecular Imaging Institute
Director, Cardiovascular Imaging
Icahn School of Medicine at Mount Sinai, New York, NY
Chronic social stress is an integral part of our busy contemporary lives. Abundant data show that severe chronic psychosocial stress is a risk factor for cardiovascular disease and a predictor of myocardial infarction and stroke. The mechanisms by which stress contributes to the higher cardiovascular event rates are primarily attributed to secondary effects on behavior, including smoking or food intake. How stress’ effect on the brain can directly impact cardiovascular disease is uncharted territory.
Preclinical data describe a direct causal link between social stress, neural signals, and atherosclerosis, the lipid-driven chronic inflammatory disease that is the underlying cause of myocardial infarction and stroke. The key connecting component is the macrophage, a large phagocytic leukocyte that originates in the bone marrow and accumulates in atherosclerotic lesions. Informed by abundant published and unpublished data, we hypothesize that chronic variable stress aggravates cardiovascular disease by interfering with macrophage dynamics.
Specifically, we wish to (1) understand how stress biologically affects macrophage dynamics in atherosclerosis; (2) develop technology that monitors macrophage dynamics non-invasively; and (3) elucidate the mechanism by which post-traumatic stress disorder (PTSD) leads to atherosclerosis.
This work is based on technological developments (such as motion compensation and fast imaging) in biomedical imaging and systems imaging using PET/MR and using novel targeted approaches (such as molecular imaging and nanomedicine) to study and treatment of inflammation in preclinical and clinical studies. I will describe our overarching and long-term goal is to collectively institute a sound scientific foundation for the biomedical and clinical community as how the link between stress and cardiovascular disease can be best approached and integrated in patient care.
REFERENCES
Date: September 19, 2017, at noon
Section Leader, Delft University of Technology, Radboud University Nijmegen, Netherlands
Founder & CEO MILabs
In biomedical preclinical research we have dreamt about a magnifying glass that would allow us to e.g. see neurotransmitters in action, that would simultaneously quantify mechanical function, perfusion and various local cell functions in the heart, and in cancer research for (simultaneous) detailed dynamic distributions of pharmaceuticals and indicators of tumor response. In recent years many groups have been involved in the development of pinhole imaging SPECT systems for imaging rodents.
At MILabs and TU-Delft, a ultra-high resolution Single Photon Emission Computed Tomography (U-SPECT-CT) has been developed that can quantify tracers in 0.15 mm structures, enable low dose imaging (sub-MBq), or visualize extremely fast tracer dynamics (sub-second time frames) by developing highly advanced imaging geometries, novel image acquisition and reconstruction. An option on this system to perform 0.6 mm Positron Emission Tomography (PET) simultaneous with 0.4mm SPECT (VECTorTM) was developed. It also enables for the first time ultra-high energy SPECT (up to 1MeV) and imaging of sub-mm resolution of theranostic isotopes to real time monitor and steer cancer therapy.
In this presentation, scientific results recorded by worldwide users of a full integrated platform combining SPECT, PET, ultra-fast and ultra-high resolution CT, Cherenkov, bioluminescence and fluorescence imaging will be discussed. Finally the results of translating U-SPECT technology into a clinical device (G-SPECT: WMIS Innovation of the Year 2015), an Ultra-fast, Ultra-high resolution (< 3 mm resolution) will be presented.
Date: September 8, 2017, at noon
Professor
Division of Physical Chemistry
Lund University, Sweden
Diffusion MRI is an excellent method for detecting microscopic changes of the living human brain, but often fails in assigning the observed changes to a specific structural property such as cell density, size, shape, or orientation. When attempting to solve this problem, we have decided to simply ignore the entire field of diffusion MRI, and instead translate data acquisition and processing schemes from multidimensional solid-state NMR spectroscopy. Key elements of our approach are q-vector trajectories and correlations between isotropic and directional diffusion encoding. To emphasize the origin of the new methods, we have selected the name “Multidimensional diffusion MRI.” Assuming that the water molecules within a voxel can be divided into groups exhibiting approximately Gaussian anisotropic diffusion, the composition of the voxel can be reported as a diffusion tensor distribution where each component of the distribution is directly related to a specific tissue environment. Our new methods yield estimates of the complete diffusion tensor distribution or well-defined statistical properties thereof, such as the mean and variance of isotropic diffusivities, mean-square anisotropy, and orientational order parameter, which are straightforwardly related to cell densities, shapes, and orientations. This presentation will give an overview of the multidimensional diffusion MRI methods, including basic physical principles, pulse sequences, data processing, and examples of applications in healthy and diseased brain.
Date: September 5, 2017, at noon
Professor of Psychiatry
University of Maryland School of Medicine
Disconnections of cortical networks may underlie various cognitive deficits that take severe clinical tolls on patients with schizophrenia. Historically, the neuropsychopharmacology of cognitive deficits is mostly conceptualized and studied in terms of neurons, neurotransmitters, and synaptic receptors. We hypothesized that the dynamics of the extended lifetime development trajectory of the brain’s white matter, and the consistency of connectivity deficits in schizophrenia, posit white matter as the key loci responsible for these cognitive deficits. Using novel diffusion weighted imaging (DWI) techniques and a milestone development of identifying key white matter tracks most relevant to schizophrenia, we are now able to show that specific white matter pathways are responsible for shared vs. unique contributions to some of the key cognitive deficits in schizophrenia.
Date: August 16, 2017, at 10:00 a.m.
Advanced Clinical Imaging Technology
Siemens Healthineers, Lausanne, Switzerland
No abstract was provided for this talk.
Date: August 8, 2017, at noon
Department of Radiology
Beth Israel Deaconess Medical Center, Boston, MA
Hyperpolarized contrast media prepared via dissolution dynamic nuclear polarization or parahydrogen-induced polarization provide tremendous in vivo signal enhancements for dilute tracer molecules labeled with nuclei such as 13C or 15N. These signal enhancements provide a tool for monitoring tissue function and metabolism, particularly in cancer and cardiac disease. In pre-clinical models of lung, prostate and breast cancer, hyperpolarized pyruvate can detect tumor response to therapy within hours of the onset of treatment, potentially providing a new tool for personalized medicine by rapidly identifying the best therapy for each patient. Clinical translation of hyperpolarized imaging will require new approaches to MR spectroscopic imaging. Spectroscopically selective balanced steady-state techniques offer improved sensitivity and speed relative to conventional echo-planar spectroscopic methods that can be leveraged for imaging in patients.
Date: August 3, 2017, at noon
MacDiarmid Institute for Advanced Materials and Nanotechnology, SCPS
Victoria University of Wellington, New Zealand
Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) is common in medical research and widely used for medical diagnosis. However, NMR and MRI systems are expensive to install and cause substantial maintenance costs. Its use is often restricted to radiology centres or hospitals in larger cities. Here we report on recent research which may help to turn inexpensive, mobile low field NMR systems into medical devices. One challenge in low field NMR is the magnetic field inhomogeneity. It introduces a distribution of Larmor frequencies and magnetic field gradients. However, field distributions can be determined (see Fig. 1 left) and may be corrected for, thus enabling these magnet systems for the use in NMR diffusometry [1]. Another challenge is the reduced signal-to-noise ratio at lower magnetic fields. Therefore, conventional imaging approaches may not be feasible. We have shown that the sample averaged fractional anisotropy (FA) can be determined without the use of imaging [2]. However, if imaging is needed, the amount of acquired data may be reduced dramatically using prior knowledge [3]. More recently we have also demonstrated that single sided NMR systems such as the NMR MOUSE [4] can be used (see Fig. 1 right) for the determination of the total volume-to-bone volume ratio, a parameter linked to the micro structure of bones and therefore to the risk factor for osteoporosis [5]. We anticipate the use of mobile low field NMR systems as diagnosis and screening tools, affordable for general practitioners as well as mobile point-of-care medical devices on the bedside, in ambulances, operational theatres and ICU’s.
Date: August 1, 2017, at noon
Research Scientist, Biomedical Translational Imaging Centre (BIOTIC), IWK Health Centre and QEII Health Sciences Centre
Assistant Professor, Departments of Diagnostic Radiology, Physics and Atmospheric Science, Microbiology and Immunology
School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
Immunotherapies are becoming increasingly important for improved treatment of a variety of cancer types. However, the development of these novel therapies has outstripped our understanding of underlying mechanisms and how best to apply them. It is therefore crucial that we use tools such as MRI, and other molecular imaging techniques, to evaluate immunotherapies in both the clinic and in preclinical studies, and develop new probes and biomarkers to increase their efficacy. Studies have shown high degrees of variability in individual response to cancer, increasing the necessity of a more personalized approach, and optimized methods for combinations of multiple therapies are not well understood.
This talk will touch on a number of molecular imaging methods used to study immunotherapy response, including the use of MRI cell tracking for monitoring both adoptive cell immunotherapies, and immune cell population migration in response to other immunotherapy subtypes. Other techniques to be discussed include use of PET (using standard FDG imaging, and novel probes specifically developed for immunotherapies) and PET/MRI multimodal imaging for monitoring both anatomical and functional changes with MRI (using DCE, T1/T2-weighted imaging, etc.).
Date: July 25, 2017, at noon
Associate Professor of Pathology
Department of Pathology, Perlmutter Cancer Center
NYU School of Medicine
Although much clinical progress has been made in harnessing the immune system to recognize and target cancer, there is still a significant lack of an understanding of how tumors evade immune recognition and the mechanisms that drive tumor resistance to both T cell and checkpoint blockade immunotherapy. Our objective is to understand how tumor-mediated signaling through inhibitory receptors, including PD-1, combine to affect the process of T cell recognition of tumor antigen and activation signaling, with the goal of understanding the basis of resistance to PD-1 blockade and the potential identification of new molecular targets to enable T cells to overcome dysfunction mediated by multiple inhibitory receptors. Potential combinatorial immunotherapeutic strategies of combining T-cell therapy strategies with checkpoint blockade will also be discussed.
Date: July 20, 2017, at noon
Computational Biomedical Imaging Group
University of Iowa
No abstract was provided for this talk.
Date: July 19, 2017, at noon
Professor of Neurology
Professor of Electrical and Computer Engineering
Professor of Physics and Astronomy
University of New Mexico
Functional connectomics using resting state fMRI (rsfMRI) is a rapidly expanding task-free approach, which has the potential to complement task-based fMRI for presurgical mapping in patients with neurological disease. However, high sensitivity to head movement and physiological noise, the low frequency range of rsfMRI (< 0.2 Hz), and considerable spatial-temporal non-stationarity compromise mapping of resting state networks (RSNs).
Recently, several studies using volumetric and multi-band high-speed fMRI have reported resting state connectivity at much higher frequencies (up to 5 Hz). This approach has the potential for addressing principal limitations of mapping low frequency resting state connectivity, such as high sensitivity to signal drifts and long scan time necessary for separating major RSNs in single subjects. However, other studies have been more cautious regarding the possible signal sources or were unable to replicate the findings.
The first part of this talk will discuss recently developed ultra-high speed fMRI and confound-tolerant seed-based resting-state fMRI analysis methodology that enabled sensitive detection of high frequency signal fluctuations in auditory cortex and default mode network. Experimental findings were validated by analyzing non-physiological signal sources using simulations of auto-correlations in Rician image noise. The second part of the talk will describe initial experience using high-speed resting state fMRI for presurgical mapping in patients with brain tumors, arteriovenous malformation and epilepsy, and integration of this approach with multi-modal diagnostic imaging.
Date: July 18, 2017, at noon
Associate Professor
Department of Biomedical Engineering
Case Western Reserve University
The focus of the Seiberlich Lab is to develop MR imaging techniques to capture structural and functional information from moving organs, specifically in the abdomen and heart. This lecture will cover the recent developments using Magnetic Resonance Fingerprinting for quantitative tissue property mapping of the myocardium. Additionally, work on real-time cardiac and abdominal imaging using non-Cartesian parallel imaging techniques in conjunction with Gadgetron will be discussed.
Date: June 29, 2017, at 10:00 a.m.
Assistant Professor, Radiology and Neuroscience
Translational and Molecular Imaging Institute
Icahn School of Medicine at Mount Sinai
This talk will cover some novel radio frequency pulse and pulse sequence designs to overcome some of the main limitations of operating at high magnetic fields, thereby enabling high-resolution whole-brain anatomical, spectroscopic and diffusion imaging. Translation of these techniques to improve diagnosis, treatment and surgical planning for a range of neurological diseases and disorders will be discussed. Specific clinical applications that will be covered include: Improved localization of epileptogenic foci; imaging to reveal the neurobiology of depression; and development of imaging methods to better guide neurosurgical resection of brain tumors.
Date: June 27, 2017, at noon
Lam Woo Professor and Chair of Biomedical Engineering
University of Hong Kong
Functional MRI (fMRI) provides the most versatile imaging platform for mapping the brain activities in vivo. More recently, resting-state fMRI (rsfMRI) has emerged as a valuable tool for mapping large-scale and long-range brain networks. However, both methods only reflect the gross outcome of the complex and cascaded activities of various cell types and networks, posing limitations when dissecting brain networks. Optogenetics technology can provide spatiotemporally precise modulation of genetically defined neuronal populations in vivo. Here we combine fMRI with optogenetic perturbations and electrophysiology to capture and analyze whole brain activity and long-range circuits with much improved specificity and causality. We deploy this capability to interrogate the spatiotemporal response properties of two distinct long-range networks, namely, thalamo-cortical and hippocampal-cortical networks. We examine the functional effects of low-frequency optogenetic stimulation within these two networks on brain responses to external sensory stimuli, on brain-wide functional connectivity at resting-state, and on cognitive behaviors. Our findings reveal that low frequency activity governs large-scale, brain-wide connectivity and interactions through long-range excitatory projections to coordinate the functional integration of remote brain regions. This low frequency phenomenon contributes to the neural basis of long-range functional connectivity as measured by rsfMRI. In this talk, I will also briefly introduce our recent diffusion MRI works in brain and MSK, including diffusion MR spectroscopy.
Date: June 16, 2017, at 12:45 p.m.
Institut Claudius Regaud
Service de médicine nucléaire
Toulouse, France
The aim of this presentation is to evaluate the use of PET/CT with 18F-FDG for an assessment of the testicular function and to optimise and standardise the acquisition protocol and the testicular volume analysis in order to do that. By ways of introduction there will be a literature overview where we establish why the 18F-FDG uptake is correlated with the spermatogenesis. There will follow an overview of the public health problem of male infertility where the different possible clinical applications for testicular functional imaging with PET/CT will be addressed.
In the second part of the talk we’ll discuss the correlation between 18F-FDG uptake in terms of intensity and volume of uptake and the testicular function via the parameters of the sperm analysis based on the published article of our group.
The third part of the presentation will be on the subject of some of the technical issues where the focus will be on the standardisation of the acquisition protocol for this specific indication. In the last part of the presentation, we’ll address the overall important subject, and even more so in this andrological context, of the radioprotection related issues of a PET/CT with 18F-FDG.
Finally, there’ll be an overview of some of the issues still to be addressed and the future perspectives.
Date: June 12, 2017, at noon
SINAPSE Chair of Clinical Radiology
University of Edinburgh
No abstract was provided for this talk.
Date: May 30, 2017, at noon
Post-doctoral Fellow
NYU School of Medicine
Small-angle X-ray scattering (SAXS) occurs when part of the X-ray beam that probes a sample is scattered at small angles, due to differences in electron density distributions within the sample. Moreover, it gives a particularly strong signal in the presence of ordered and periodic systems. The recently developed small-angle X-ray scattering tensor tomography (SAXSTT) takes X-ray tomography a step further: it uses two sample rotation axes and an iterative reconstruction algorithm to tomographically reconstruct local tissue anisotropy. The method was demonstrated for reconstructing the orientation of mineralized collagen fibrils in bone trabeculae of human vertebrae, based on the 65-nm D-spacing of collagen. Similar experiments have also very recently been performed in mouse brain, taking advantage of the ~17.5 nm spacing of the myelin sheath. Providing directly structural information, SASTT was used to derive neuronal fiber directionality and myelin content in a quantitative way. The results are being compared with MRI methods such as diffusion-weighted imaging and magnetization transfer, as well as with 2D and 3D histology (CLARITY).
Date: May 25, 2017, at 10:00 a.m.
Associate Professor, Principal Investigator
University of California, San Francisco
MRI has historically performed poorly when imaging ultra-fast relaxing tissues such as bone, lung tissue, and tendons as well as components of other connective tissues including cartilage and myelin. Specialized pulse sequences such as ultrashort echo time (UTE) and zero echo time (ZTE) MRI offer the potential to image these tissues, and have several promising new applications that will broaden the capability of MRI. These include:
Date: May 16, 2017, at noon
PhD Candidate
Sackler Institute of Graduate Biomedical Sciences
NYU School of Medicine
Diffusion MRI is sensitive to the length scale of tens of microns, which coincides with the scale of microstructure in the human brain tissue. By changing the diffusion time or diffusion gradient pulse width, we can probe the brain micro-geometry via time-dependent diffusion measurements. To increase the sensitivity to the microstructure, STEAM sequence is often used for extending the range of diffusion time. However, water exchange between myelin water and intra-/extra-axonal water may bias the parameter estimations. This talk will focus on the time dependence either along or perpendicular to white matter axons and corresponding micro-geometries, and the correction for the time dependence measured by STEAM.
Date: May 9, 2017, at noon
PhD Candidate
Sackler Institute of Graduate Biomedical Sciences
NYU School of Medicine
Diffusion of water molecules is directly influenced by the mountainous landscape of biological tissue. By modeling time-dependent diffusion, it is possible to reverse engineer various features of this landscape. The proposed model will depend on the underlying tissue microstructure, which poses an additional challenge of model selection. This talk will focus on the efforts of modeling diffusion time-dependence in the prostate, which embodies modeling problems that concern partial volume and model selection, as well as imaging problems, such as geometric distortion and low SNR.
Date: April 18, 2017, at noon
Assistant Professor of Radiology
NYU School of Medicine
PET radiochemistry can be a great resource for imaging, treatment and point-of-care response/monitoring in cancer and cardiovascular disease. A novel small molecule targeting the cyclin-dependent kinases CDK4/6 and a series of radiolabeled nanobodies and peptides for atherosclerotic plaques imaging will be presented. The seminar will also focus on the fundamentals of radiochemistry and how the newly established CAI2R Radiochemistry facility will operate.
Date: April 14, 2017, at noon
Assistant Professor of Biomedical Engineering
Co-director, NeuroPoly, École Polytechnique, University of Montreal
Producer of MRM Highlights and founder of OHBM blog
Over the past decade, the number of microstructural imaging papers has been doubling every 2.7 years. With such growth, it is becoming increasingly difficult to perform a fair comparison between competing approaches. Some simplify the tissue modelling and overlook physiological constraints. Others overparametrize the models and amplify the noise. The outcome is a field of research with great promise, but few checks and balances.
This lecture will introduce several frameworks for interpreting, validating and communicating microstructural imaging data. Examples will be drawn from myelin imaging in the brain, focusing on the challenges associated with mapping the longitudinal relaxation time (T1), the axon caliber, and the myelin thickness (g-ratio). The last part of the lecture will put these frameworks in a broader science communication context, discussing how medical imaging researchers can set new standards for reviewing, publishing, and publicizing their findings.
Date: April 10, 2017, at noon
Assistant Professor of Radiology
Johns Hopkins University
Recently, Chemical Exchange Saturation Transfer (CEST) has emerged as an attractive MRI contrast mechanism. In CEST, the MRI contrast is generated by transferring the magnetic labeled water-exchanging protons (OH, NH, or NH2) from a CEST agent to its surrounding water molecules. Many natural biological compounds naturally carry exchangeable protons, making them possibly detected by CEST MRI directly in a “label-free” manner. In our studies, we utilized this unique feature to directly detect drugs and drugs carriers, which makes MRI-guided drug delivery possible even without any chemical labeling, a strategy we called “natural labeling”. This new MRI labeling strategy in principle can be tailored to many existing drug delivery systems, and portends a new path to safe, rapid clinical translation of image-guided drug delivery.
Date: April 4, 2017, at noon
Graduate Student
Sackler Institute of Graduate Biomedical Sciences
NYU School of Medicine
A high-throughput imaging pipeline is presented to characterize the heterogeneity in longitudinal disease progression in mouse models of human brain cancer and to test the efficacy of novel anti-cancer therapeutics in accurate mouse models of sporadic human cancer.
Date: March 28, 2017, at noon
Assistant Research Scientist
Center for Biomedical Imaging
Department of Radiology
NYU Langone Medical Center
Huntington disease (HD) is a dominantly inherited and progressive neurodegenerative disorder, caused by a CAG trinucleotide repeat expansion (≥ 39 repeats) within the HD gene. The median age at which HD occurs is around 40 years, and the disease progresses over time and is invariably fatal 15–20 years after the onset of the first symptoms. The major goals of current HD research are to improve early detection and monitor pathological changes in individuals both at early and advanced stages of the disease. Animal models of inherited neurological diseases provide an opportunity to test potential biomarkers of disease onset and progression and evaluate treatments for translation to clinical care. Using several diffusion MR techniques, we studied two different rat models of HD. In this talk, I will present data that shows that diffusion MRI is a sensitive and quantitative method to detect HD-related neurodegenerative changes, at both microstructural and subcellular levels.
Date: March 7, 2017, at noon
Chief, Magnetic Resonance Imaging and Spectroscopy Section
NIH/National Institute on Aging
There is an ongoing need for non-invasive identification of macromolecular changes in tissue. An important application is to the diagnosis of early osteoarthritis (OA). Our work in this area combines basic science studies in magnetic resonance imaging and relaxometry with emerging methodologies that carry translational potential. We will discuss multi-exponential transverse relaxation analysis as a means to identify underlying macromolecular compartments in normal and degraded cartilage, as well as important extensions of this work, based on higher dimensional relaxometry and compressed sensing. We will describe the mathematical setting for this work as a linear inverse problem. Further work in human subjects requires introduction of a nonlinear model system. We will describe several approaches to these problems and indicate the potential for improved detection of early cartilage degradation. Our methods are also applicable to directly mapping myelin in the brain, and we have obtained results showing myelination pattern alterations with age and in cognitive impairment. All of these studies are centered around the clinical goal of improving the ability of magnetic resonance methods to diagnose pathology and to monitor disease progression.
Date: February 14, 2017, at noon
Associate Professor of Radiology
New York University School of Medicine
Separately, PET and MRI have longstanding roles in diagnosis, prognosis and monitoring of breast cancer. Since the recent advent of the simultaneous PET/MRI platform, intense research has taken place to identify unrealized applications of their fusion. Initial work around the world has included study of a range of practical advantages (feasibility, efficiency, patient retention, physiologic simultaneity, co-registration), but always with an eye toward future ‘breakout’ applications beyond those with separate scans. I will describe efforts within our breast cancer research group that pursue both practical and fundamental benefits with the unique capabilities in our research center. Whole body evaluation of metastatic breast cancer patients is nearly equivalently done with PET/MRI as with PET/CT but with half the radiation dose. Dynamic contrast enhanced (DCE) MRI and intra-voxel incoherent motion (IVIM) MRI offer a range of quantitative characterizations of the primary tumor microenvironment (cellularity, vascular volume, vascular permeability) that when combined with fluorodeoxyglucose (FDG) uptake provide a comprehensive characterization of malignancy in one imaging session. Simultaneity also supports detailed intralesional correlations that may increase classification ability even further. Finally, future planned work with more specific microenvironment tracers and integrated PET and MRI pharmacokinetic modeling holds remarkable potential for oncologic management with noninvasive imaging.
Date: February 6, 2017, at noon
Fraunhofer MEVIS
Bremen, Germany
Analyzing moving organs such as the heart in MRI is a challenging task. In clinical routine images are acquired over several heartbeats to reconstruct all contraction phases of one representative cardiac cycle using ECG-gating and breath-hold techniques.
Real-time MRI techniques allow the acquisition of serial 2D images with a temporal resolution of up to 20 ms under free breathing. The analysis of real-time MRI sequences, however, requires adapted segmentation techniques as well as an advanced analysis providing information about temporal evolution of parameters during individual heart cycles in a multi-cycle analysis workflow.
Date: February 1, 2017, at noon
Research Associate
Biomedical Engineering Department
King’s College London, UK
No abstract was provided for this talk.
Date: January 24, 2017, at noon
Assistant Professor, Director of Brain Mapping
Department of Radiology
New York University School of Medicine
No abstract was provided for this talk.
Date: January 23, 2017, at noon
William L. Root Professor of EECS
University of Michigan
Many problems in signal and image processing, machine learning, and estimation require optimization of convex cost functions. For convex cost functions with Lipschitz continuous gradients, Nesterov’s fast gradient method decreases the cost function at least as fast as the square of the number of iterations, a rate order that is optimal. This talk describes a new first-order optimization method called the optimized gradient method (OGM) that converges twice as fast as Nesterov’s famous method yet has a remarkably similar simple implementation. Interestingly, Drori recently showed that OGM has optimal complexity among first-order methods. I will discuss other recent extensions and show examples in machine learning and X-ray computed tomography (CT). Combining OGM with ordered subsets provides particularly fast reconstruction for CT. This work is joint with Donghwan Kim.
Date: January 10, 2017, at noon
Professor of Ophthalmology
Director, Ocular Imaging Center
NYU Langone Medical Center
Professor and Chairman of Ophthalmology
Professor of Neuroscience and Physiology
NYU Langone Medical Center
Professor of Electrical and Computer Engineering
NYU Tandon School of Engineering
Professor of Ophthalmology
Director, Ophthalmic Imaging Research Laboratory
Vice Chair for Clinical Research
NYU Langone Medical Center
In recent years ocular imaging has become the cornerstone for clinical diagnosis and disease monitoring as well as a primary research tool in ophthalmology. In this presentation we will discuss state-of-the-art, in-vivo, high resolution ocular imaging technologies. We will present the utility and challenges of the technologies to advance clinical practice and research of glaucoma—a leading cause of blindness and visual morbidity.
Date: January 6, 2017, at noon
Director, Center for Clinical Spectroscopy
Department of Radiology, Brigham and Women’s Hospital
Assistant Professor, Harvard Medical School
Advances in neuroimaging provide us with greater insight to brain injury than ever before. Magnetic resonance spectroscopy is a non-invasive method of measuring brain chemistry altered by brain injury using readily available MRI, thus providing a virtual biopsy of concussions. A review of the technology and current findings from the acute to chronic stages of mild brain injury, including the rising concern of chronic traumatic encephalopathy in sports and military-related repetitive brain trauma, will be discussed.
Date: Tuesday, December 13th at noon
Professor of Ophthalmology
Director, Ophthalmic Imaging Research Laboratory
Vice Chair for Clinical Research
NYU Langone Medical Center
Professor and Chairman of Ophthalmology
Professor of Neuroscience and Physiology
NYU Langone Medical Center
Professor of Electrical and Computer Engineering
NYU Tandon School of Engineering
Professor of Ophthalmology
Director, Ocular Imaging Center
NYU Langone Medical Center
In recent years, ocular imaging has become the cornerstone for clinical diagnosis and disease monitoring as well as a primary research tool in ophthalmology. In this presentation, we will discuss state-of-the-art, in-vivo, high-resolution ocular imaging technologies. We will present the utility and challenges of the technologies to advance clinical practice and research of glaucoma—a leading cause of blindness and visual morbidity.
Date: Monday, November 21st at noon
Institute for Computational and Applied Mathematics
University of Münster
In this talk, we discuss the use of advanced physical modeling to build successful image reconstruction approaches for dynamic imaging, including motion, noise, and undersampling. The variational approach is based on minimizing energy functionals in a spatio-temporal domain, including advanced models of the image formation process, noise, and motion. For the latter, hyperelastic or fluid-dynamic constraints are used to jointly estimate feasible motion vectors with the image sequence. We present the potential use of these methods for dynamic PET and highly undersampled dynamic CT. Finally, we comment on extensions to include cross-modality information, such as is available in PET-MR. We discuss potential issues when using pre-estimated motion vectors from the MR sequence and propose a mathematical model to overcome those.
Martin Burger received a MS (Diplom) in Mathematics (1998) and a Ph.D. in Mathematics (2000) from the Johannes Kepler University Linz. After a period as CAM assistant professor at UCLA, he returned to Johannes Kepler University, where he completed his habilitation in Mathematics (2005). Shortly afterward, he was offered a full professor position in applied mathematics at the Westfälische Wilhelms-Universität Münster, where he moved in 2006. Since then, he has led the mathematical imaging group and contributed significantly to building interdisciplinary research structures related to biomedical imaging. He serves as PI, executive board member, and research area coordinator for the Cluster of Excellence "Cells in Motion," funded by the German Science Foundation (DFG). He has received several awards and recognitions, including the Calderon Prize 2009 from the Inverse Problems International Association, an ERC consolidator grant in 2013, and an offer to become director of the Weierstrass Institute for Applied Analysis and Stochastic in Berlin. His research focuses on mathematical imaging and inverse problems, currently with an emphasis on dynamic and high-dimensional image reconstruction.
Date: Friday, November 18th at noon
MRI of motion-sensitive applications such as abdominal examinations usually relies on strict breath-holding. However, breath-holding can fail, especially for sick, elderly, or pediatric patients, which can render image quality non-diagnostic. Furthermore, sudden motion events such as global body shifts, bulk motion, or coughing may induce further artifacts.
This talk presents methods which solve these problems and enable motion-robust free-breathing MR acquisitions. First, recent advances for comprehensive one-stop shop free-breathing imaging are presented by Thomas Benkert. Second, a technique to automatically detect and exclude bulk motion is described by Bjorn Stemkens. In summary, the presented techniques have the potential to increase patient comfort, improve clinical workflow, and eliminate the risk of failed exams caused by imperfect breath-holding or sudden patient movements.
Dr. Thomas Benkert obtained his PhD in Physics at the University of Wuerzburg, where he worked on novel steady-state techniques for fast MRI under the supervision of Dr. Felix Breuer. In August 2015, he joined CBI as a postdoctoral researcher in the team of Dr. Tobias Block, where he is developing methods for comprehensive free-breathing imaging.
Bjorn Stemkens is a PhD candidate at the Department of Radiotherapy at the University Medical Center in Utrecht, where he is working on the implementation of novel MRI applications for MRI-guided radiotherapy, with a focus on abdominal treatments. In July, he began a 4-month internship at CBI in the team of Dr. Tobias Block to develop a technique to detect bulk motion for robust free-breathing abdominal imaging.
Date: Tuesday, November 15th at noon
Professor, Department of Ophthalmology
Director, Small-Animal MRI Facility
Wayne State University School of Medicine
In 1992, it was not obvious that MRI, a relatively insensitive and still developing imaging method, would be useful for examining the retina, one of the thinnest organs in the body. Since then, Dr. Berkowitz has established a body of work that highlights MRI as a surprisingly useful discovery tool in vision research. These methods have been successfully transitioned into cancer and brain research areas and are used by drug companies and other investigators worldwide. Improvements in resolution and methodology have even allowed us to measure the physiology of sub-compartments within rod cells in vivo. These data are spatially grounded based on optical coherence tomography images and compared to visual performance using optokinetic tracking. His current pioneering efforts use MRI to measure neuronal oxidative stress without a contrast agent in untreatable neurodegenerative diseases, including Alzheimer’s disease, to optimize antioxidant treatment in vivo.
Date: Tuesday, November 8th at noon
Assistant Professor of Radiology
Johns Hopkins University School of Medicine
Assessment of intrinsic brain networks using resting state functional MRI (rs-fMRI) has resulted in a paradigm shift in evaluating brain function. Changes in functional connectivity have been described in numerous disorders, and normal intrinsic brain networks characterized in thousands of subjects. Several studies have examined the use of rs-fMRI in presurgical brain mapping. Following an overview of rs-fMRI basics, the benefits of rs-fMRI over task-fMRI in presurgical brain mapping will be discussed. Challenges in characterizing rs-fMRI at the single subject level for presurgical brain mapping will be reviewed.
Haris Sair, MD, is an Assistant Professor of Radiology in the Department of Radiology at Johns Hopkins University School of Medicine. He completed a 2-year fellowship in Neuroradiology at the Massachusetts General Hospital, where he developed an interest in clinical functional MRI. His primary research interest is in the application of resting state fMRI at the single subject level for clinical use, concentrating on presurgical brain mapping, but also including development of rs-fMRI based prediction models in disease and outcome.
Date: Tuesday, October 25th at noon
Departments of Biomedical Engineering and Radiology
Columbia University in the City of New York
My laboratory pursues technology and method developments in the fields of magnetic resonance imaging (MRI) and spectroscopy (MRS) to advance their clinical potential for the study of multiple sclerosis (MS) and other neurological disorders. MRI and MRS allow the non-invasive measurement of brain anatomy and physiology, but excellent B0 magnetic field homogeneity is required for meaningful results. In the first part of my talk, I will present a technique for magnetic field modeling and correction, i.e., shimming, that is based on the combination of fields generated by an ensemble of individual, generic coils. This multi-coil approach enables the accurate generation of simple and complex magnetic field shapes in a flexible fashion. B0 shimming with the dynamic multi-coil technique (DYNAMITE) is shown to outperform conventional methods based on spherical harmonic (SH) functions and provides unrivaled magnetic field homogeneity in mouse, rat, and human brain.
MS is a chronic disorder of the central nervous system that leads to demyelination and neurodegeneration. Its underlying pathobiochemical mechanisms, however, remain poorly understood. MRS promises non-invasive access to the brain's biochemistry in vivo, but suffers from methodological limitations and experimental imperfections. The goal of our work is to establish MRS as a clinical research tool towards in vivo metabolomics of the pathogenesis of MS through a combination of ultra-high 7 Tesla field, state-of-the-art B0/B1 shimming, and optimized MRS methods. The second part of my talk will focus on the specific MRS infrastructure and implementations that enable us to assess pathological changes from the earliest stage of the disease.
Date: Tuesday, October 18th at noon
Professor of Neurology
University of Pittsburgh School of Medicine
This talk will focus on Dr. Pan's work in the development and application of high field imaging approaches to better understand the metabolic and functional pathophysiology of epilepsy. These methods include high degree B0 shimming, high resolution MRSI, and in vivo detection of amino acids and will discuss some of her results from 7T and 3T.
Assistant Professor of Radiology
Department of Radiology
Center for Biomedical Imaging
Although MRI assessment of white matter lesions is essential for the clinical management of multiple sclerosis, the processes leading to the formation of lesions and underlying their subsequent MRI appearance are incompletely understood. We used proton MR spectroscopy to study the evolution of N-acetyl-aspartate (NAA), creatine (Cr), choline (Cho), and myo-inositol (mI) in pre-lesional tissue, persistent and transient new lesions, as well as in chronic lesions, and related the results to quantitative MRI measures of T1-hypointensity and T2-volume. Within 10 patients with relapsing-remitting course, there were 180 regions-of-interest consisting of up to seven semi-annual follow-ups of normal-appearing white matter (NAWM, n=10), pre-lesional tissue giving rise to acute lesions which resolved (n=3) or persisted (n=3), and of moderately (n=9) and severely hypointense (n=6) chronic lesions. Compared to NAWM, pre-lesional tissue had higher Cr and Cho, while compared to lesions, pre-lesional tissue had higher NAA. Resolving acute lesions showed similar NAA levels pre- and post-formation, suggesting no long-term axonal damage. In chronic lesions, there was an increase in mI, suggesting accumulating astrogliosis. Lesion volume was a better predictor of axonal health than T1-hypointensity, with lesions larger than 1.5 cm3 uniformly exhibiting very low (<4.5 millimolar) NAA concentrations. A positive correlation between longitudinal changes in Cho and in lesion volume in moderately hypointense lesions implied that lesion size is mediated by chronic inflammation. These and other results are integrated in a discussion on the steady state metabolism of lesion evolution in Multiple Sclerosis, viewed in the context of conventional MRI measures.
Ivan Kirov received his Bachelor of Science in Biology from the University of California, Irvine. After graduation, he worked for 2 years as a molecular biologist on retinal stem cells. In 2004 he entered the Ph.D. program at the Sackler Institute at NYU, graduating in 2009 from the program in Physiology and Neuroscience. He then completed a post-doctoral fellowship under Oded Gonen, training on applications of proton MR spectroscopy. Ivan has been an independent investigator since 2014 as an Assistant Professor with research interests mainly in Traumatic Brain Injury and Multiple Sclerosis.
Date: Tuesday, September 20th at noon
Postdoctoral Associate
Center for Biomedical Imaging
Department of Radiology
NYU School of Medicine
Magnetic Resonance Imaging (MRI) is a reference method for noninvasive examination of the global and local cardiac function. Using the latest real-time MRI sequences, cardiac function can be monitored over multiple consecutive heart beats, enabling the study of cardiac cycle variability, for example, in patients with arrhythmia. An essential precondition for the analysis of cardiac function is the segmentation of the heart muscle (myocardium). To address this task, a hierarchical object-based segmentation approach was devised, which combines bottom-up region grouping with a top-down optimization strategy. This principle takes steps towards bridging the semantic gap between low-level image features and high-level, complex and heterogeneous structures. This algorithm is part of a comprehensive pipeline for automatic segmentation of the myocardium from short-axis MRI. Furthermore, tissue phase mapping (TPM) offers the means to inspect local cardiac motion by acquiring the velocity of individual myocardium voxels. This talk presents a semi-automatic probabilistic segmentation approach for TPM that combines contour displacement with particle tracing for estimating the uncertainty of the segmentation result. An automatic quantification method was additionally developed to compute global myocardial torsion.
Teodora Chitiboi received her PhD in Computer Science from Jacobs University Bremen, after having received a Bachelor and Master in Computer Science. Teodora was a researcher at Fraunhofer MEVIS in Bremen where she contributed to the development of an object-based image analysis (OBIA) library and was part of the group for Cardiovascular Research and Development. Her research interests are medical image analysis, visualization, and image segmentation.
Assistant Professor
Department of Diagnostic Radiology and Nuclear Medicine
University of Maryland, Baltimore
This talk presents advanced MRI methods for two cardiovascular applications: non-contrast-enhanced (NCE) angiography and cardiac late gadolinium enhanced (LGE) imaging. First, I will present new NCE MRA methods using Fourier-based velocity-selective (VS) magnetization preparation, which can generate positive vessel contrast in a single acquisition with high spatial resolution in all three dimensions. The principle of VS excitation is explained under excitation k-space formalism, followed by a few designs with improved B0 and B1 immunity, and applications for various vascular territories. Second, I will present 3D LGE MRI methods based on stack-of-spirals acquisitions. Two strategies will be shown, including single breath-hold whole-heart LGE using parallel imaging acceleration, and free-breathing near-isotropic resolution LGE using outer-volume-suppressed projection-based navigator.
Date: July 25th at noon
Associate Professor
Department of Physics
Norwegian University of Science and Technology
Diffusion-weighted MRI (DWI) has become a standard component in most clinical breast MRI protocols. The main reason for this is the reduced apparent diffusion coefficient (ADC) observed in cancer tissue compared to healthy fibroglandular tissue and benign lesions. This effect is loosely attributed to increased cellularity and reduced extracellular volume in cancer tissue. Other flavors of DWI, like diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and most recently stimulated echo diffusion imaging (STE-DWI), have also been applied in breast cancer MRI. Each of these methods shows some interesting results, including differences between malignant and benign tissues.
Since DWI draws its contrast from the microscopic structural features of the tissue, the link between DWI and specific microstructural parameters has always been a topic of interest. However, due to the non-unique mapping from DWI signal to microstructure, this has proven very difficult in practice. As an example, only weak correlation between cellularity and ADC has been shown in breast cancer. A biophysical interpretation of the DWI signal is therefore often avoided, and the results from DWI are analyzed with respect to its link with other parameters, like malignancy/grade or molecular subtype.
In this presentation, we will discuss this issue in some detail, looking at the structure of healthy and malignant tissues together with results from the various methods in DWI.
Pål Erik Goa obtained his PhD in Physics at the University of Oslo, Norway, in 2002, after building the first microscope capable of capturing the live motion of quantized magnetic flux lines in a superconductor. After working on remote sensing applications for the Norwegian Defence Research Establishment for a couple of years, he and his family moved to Trondheim in 2005, and he started on a new career in the field of medical imaging. In the period 2006-2013, he worked as an MR physicist at the Department of Radiology and Nuclear Medicine, St. Olavs Hospital, and in 2013 he took up the position as Associate Professor in Medical Physics at NTNU. Goa has been involved in a wide variety of research projects in MRI, both clinical and pre-clinical, ranging from retro-gated cardiac MRI in mice to the development of new sequences for BOLD-fMRI at 7 T. His current research interest is focused on the application of different methods of Diffusion-Weighted MRI in Cancer Management.
Date: Tuesday, June 14th at noon
Assistant Professor of Radiology
Center for In Vivo Microscopy
Duke Medical Center, Durham, NC
The rich contrast and flexibility of MR offers the possibility of quantifying multiple image-based biomarkers in small animal models of neurological and psychiatric conditions. This talk focuses on a mouse model of Alzheimer’s disease (AD). Mouse models provide opportunities to study characteristics of AD in well-controlled environments and can facilitate early interventions. Multivariate biomarkers are needed for detecting AD, helping to understand its etiology, and quantifying the effect of therapies. The CVN-AD mouse model replicates multiple AD hallmark pathologies, and we identified multivariate biomarkers characterizing a brain circuit disruption predictive of cognitive decline. We used manganese-enhanced MRI to locate areas of differential uptake of manganese in CVN mice relative to age-matched controls, and in association with learning and memory deficits. In vivo and ex vivo MRI revealed that CVN-AD mice replicate the hippocampal atrophy (6%), characteristic of humans with AD, and also present changes in subcortical areas. The largest effect was in the fornix (23% smaller), which connects the septum, hippocampus, and hypothalamus. In characterizing the fornix with diffusion tensor imaging, fractional anisotropy was most sensitive (20% reduction), followed by radial (15%), and axial diffusivity (2%) in detecting pathological changes. These findings were strengthened by optical microscopy and ultrastructural analyses. CD68 staining showed that white matter pathology could be secondary to neuronal degeneration or due to direct microglial attack. In conclusion, these findings strengthen the hypothesis that the fornix plays a role in AD and can be used as a disease biomarker and as a target for therapy.
Dr. Badea is an Assistant Professor in the Department of Radiology at Duke and a member of the Center for In Vivo Microscopy, where she co-directs the Visual Informatics Core while maintaining her focus on models of neurodegenerative conditions. She was born in Romania, where she studied Physics for her BSc. She graduated with a PhD in Biomedical Engineering from the University of Patras, Greece, where she learned to love working with images, and in particular, brain images. Her interest in computational imaging has led to the development of image processing pipelines for structural and diffusion imaging. She uses such pipelines with the aim to understand the lessons that small mouse models can teach us about human neurological and psychiatric conditions.
Date: May 31st at noon
Doctoral Candidate
Research Assistant
Graz University of Technology
Compressed sensing techniques allow MRI reconstruction from undersampled k-space data. However, most reconstruction methods suffer from high computational costs and are limited to low acceleration factors for non-dynamic 2D imaging protocols. Furthermore, existing image reconstruction methods are based on simple regularizers such as sparsity in the wavelet domain or Total Variation (TV). However, these simple and handcrafted regularizers make assumptions on the underlying image statistics, and the reconstructed images appear unnatural. In this work, we propose a novel and efficient approach to overcome these limitations by learning a sequence of optimal regularizers that removes typical undersampling artifacts while keeping important details in the imaged objects and preserving the natural appearance of anatomical structures. We test our approach on patient data and show that we achieve superior results in terms of both runtime and image quality compared to commonly used reconstruction methods.
Kerstin Hammernik received a BSc and MSc in Biomedical Engineering from Graz University of Technology in 2011 and 2015, respectively. Currently, she is a research assistant and PhD student supervised by Dr. Thomas Pock at the Institute of Computer Graphics and Vision, Graz University of Technology. Her current research interests include optimization and learning of variational models with application to medical inverse problems such as magnetic resonance (MR) and photoacoustic image reconstruction.
Date: May 27th at noon
Assistant Professor of Biomedical Engineering
Vanderbilt University Institute of Imaging Science (VUIIS)
Nashville, TN
Many-coil transmit arrays are desirable in parallel transmission (pTx), since with many coils multidimensional pulses can be shortened, more uniform radiofrequency shims can be produced, and specific absorption rate can be more effectively controlled. However, the high cost and the large physical footprint and cabling requirements of the corresponding power amplifiers required to drive many-coil arrays has limited the number of transmit coils/channels that are used in practice, and most ultra-high field MR scanners in use today have only eight transmit channels. Inspired by recent work in MRI receive array compression, we proposed array-compressed pTx (acpTx) to overcome these limitations. In acpTx, a large number of coils is connected to a small number of channels via a virtual or physical array compression network that splits the input pulse waveforms to the coils and applies attenuations and phase shifts that are optimized jointly with the pulse waveforms. In this way, the excitation spin physics directly informs the construction of the compressed transmit coil array. This talk will describe how pulses can be designed for acpTx and how it can be implemented in hardware. We will also talk about its potential embodiments and impact on ultra-high field MRI, including how it might be used to improve transmit coil design.
Date: May 26th at noon
Research Director
Associate Research Director
Research Manager
Member of the Research Staff
Riverside Research has been engaged in biomedical research since it was the Electronics Research Laboratory of Columbia University in the 1950s. Over the past several decades, the Biomedical Engineering Laboratory at Riverside Research has become internationally recognized as a leader in advanced biomedical ultrasound. The history, capabilities, and interests of the Laboratory will be summarized.
High-frequency ultrasound annular-array probes operating at frequencies higher than 15 MHz provide resolution superior to linear arrays operating at the same frequencies. We have developed custom imaging systems based on five-ring, 20-MHz and 40-MHz annular arrays, and have shown that the devices permit a significant improvement in image quality over current technology for small-animal- and ophthalmic-ultrasound imaging. An overview of the systems and examples of in vivo human and in utero mouse-embryo scans will be shown. Extensions of the work to photoacoustic imaging of mouse embryos as well as applications such as characterization of the human vitreous and analysis of brain development in mouse embryos will be discussed.
Quantitative ultrasound (QUS) methods permit characterizing tissue microstructure at a sub-resolution level. Our group is considered to be a pioneer in using these methods for tissue characterization. As an example, a high-frequency ultrasound study focusing on 3D imaging and characterization of lymph nodes freshly-excised from cancer patients will be presented. QUS images were formed and used to detect metastases using a transducer that has a 26-MHz center frequency. Classification results suggest that these QUS methods may provide a clinically-important means of identifying small metastatic foci that might not be detected using standard pathology procedures.
Scanning acoustic microscopy (SAM) is a well-established method for fine-resolution material characterization in particular for non-destructive testing. However, accurate estimation of the mechanical properties for soft-tissue applications is still challenging. We developed a novel quantitative acoustic microscope (QAM) operating at 250-MHz and 500-MHz center frequencies that allows characterizing soft-tissue material properties (i.e. mass density and bulk modulus) at resolutions down to four micrometers. The presentation will provide an overview of the device and its working principle. We will present current research results obtained for ophthalmologic tissues and human lymph nodes, and will discuss the potential applications for the measured material properties.
Date: May 3rd at noon
Professor of Radiology and Imaging Sciences
The University of Utah
Salt Lake City, Utah
Radiation dose associated with CT scans has become an important concern in medical imaging. Fortunately, there are many pathways to reducing dose, one of which amounts to using a model-based iterative reconstruction method. A major strength of this approach is its flexibility: there are many ways to design such a reconstruction, allowing adaptation to both anatomy and diseases. This strength however comes with major challenges in terms of gain assessment. Early assessment of image quality, before clinical deployment, is critical to identify and refine solutions. Moreover, given the non-linear nature of model-based iterative reconstruction methods, task-based assessment must be embraced, which further complicates the problem. Currently, there are few publications reporting on early, task-based assessment of image quality achieved with iterative reconstruction methods. This talk will present results in this direction using LROC analysis with computer-simulated data read by human observers. At the same time, it will be demonstrated that the grayscale used for image display is a critical factor in such image quality comparison studies.
Frederic Noo is a Professor of Radiology and Imaging Sciences at The University of Utah. His education took place in Belgium, where he completed a Ph.D. degree in Engineering Sciences in 1998, with emphasis on image reconstruction problems in cone-beam tomography. The National Science Foundation in Belgium supported his research from 1993 until 2001, first as a Ph.D. student, then as a post-doctoral researcher. In 2001, he decided to move to The University of Utah, where he had built strong collaboration ties. Since then, he has expanded his range of expertise to encompass all aspects of X-ray computed tomography, including image reconstruction algorithms, scanner design, simulation models and Monte-Carlo transport of photons, noise and dose evaluations, and task-based assessment of image quality using both model and human observers. His publications include 65 peer-reviewed articles, 112 conference proceedings, and 9 patents. His CT expertise is recognized in the industry as well as in academia. He launched a highly successful biennial stand-alone conference in 2010, called "The International Conference on Image Formation in X-ray Computed Tomography". This effort was offered as a community service to address a growing need for CT scientists. His work has been continuously supported by the NIH and by corporate funds since 2001. He has supervised a number of Ph.D. students and post-doctoral researchers, who have become prolific scientists with Siemens, GE, Philips, and the FDA. A number of his image reconstruction methods are or have been used by vendors; and the FDA supports his methods for image quality assessment.
Date: April 26th at noon
Graduate Student
Biomedical Imaging Program
Sackler Institute, Radiology Department
NYU School of Medicine
Three-dimensional (3D) printing in radiology represents the fabrication of physical objects from imaging data, with the intent of impacting patient care. 3D printing of anatomical data allows radiologists, surgeons, and other physicians to physically hold in their hands patient-specific models and use visuo-haptic inputs to better understand both complex anatomy and the condition being treated. In this talk, I will describe the steps required to derive anatomically accurate, patient-specific models in the context of urological oncology. In particular, the application of 3D printing in the pre-operative evaluation of prostate and kidney cancer will be demonstrated.
Nicole Wake received her Bachelors in Biology and History from the University of Pennsylvania and her Masters in Science from the Mount Sinai School of Medicine. She has extensive experience working as a research assistant in a cardiovascular CT and MR imaging lab at Brigham and Women's Hospital, Boston, MA. Nicole is currently a PhD Candidate at NYU School of Medicine, where she works under the direction of Hersh Chandarana and Daniel K. Sodickson on applications of 3D printing in urologic oncology.
Date: April 19th at noon
Research Associate
Department of Radiology
Johns Hopkins University School of Medicine
Diffusion MRI is a powerful tool for noninvasive mapping of the microstructural organization in the brain. One part of my work focuses on developing high-resolution in vivo imaging techniques to resolve structures and connections in the live mouse brain. With a localized high-resolution imaging technique, we achieved in-utero diffusion MRI of the embryonic mouse brain. I also worked on probing brain microstructural features using oscillating gradient diffusion MRI (OGSE). In a neonatal mouse model of hypoxia-ischemia, OGSE diffusion MRI showed drastic change of contrast in the edema tissue and enhanced sensitivity in mild edema region compared to conventional pulsed gradient diffusion MRI. We have explored the diffusion-time dependence of kurtosis property of water diffusion and the time dependence of intra-voxel incoherent motion at varying oscillating frequencies. These work may lead to better understanding of the relation between diffusion MRI signals and the underlying tissue microstructural properties.
Dr. Wu obtained Masters and PhD degrees from Johns Hopkins University, Department of Biomedical Engineering, where she conducted the thesis study mainly on diffusion MRI. Currently, she is a Research Associate in the Department of Radiology at Hopkins, starting her independent research in the technical development and biomedical applications of advanced diffusion MRI techniques.
Date: April 8th at 2:00 p.m.
Postdoctoral Researcher
Rush University
Chicago, IL
MR microscopy has developed over the last 25 years as a complementary microimaging technique. Although it offers the potential to study tissues in vivo, the inherently low sensitivity of NMR has limited MR microscopy to the study of relatively large cells, i.e. frog ova (~1mm in diameter) and Aplysia neurons (~ 300-350 μm in diameter). Recently, using new surface microcoils and high field magnets to improve sensitivity, we performed the first MR microscopy of neurons in mammalian tissue, and potential identification of mammalian nuclei in the tissue. These findings have the potential to change the way we interpret clinical MR images by revealing unique signal and contrast characteristics of the microstructural elements that comprise tissues: perikarya, nuclei, neurites, vasculature etc. Developing a better understanding of subcellular elements and how their MR characteristics change under the influences of pathology will lead to advances in tissue modeling and provide diagnostic criteria for earlier and more accurate disease detection. Such improvements are critically needed in the case of neuropathologies which often present with abnormalities at the cellular level many years prior to the development of symptoms which spur patients to seek treatment. In this work, we offer an overview of the progress made in MR microscopy of neural tissues and non-neural tissue applications, and potentials of offering a better references to clinical treatment.
Date: April 5th at noon
Associate Professor
Russell H. Morgan Department of Radiology and Radiological Science
Division of MR Research and the Institute for Cell Engineering
Johns Hopkins University
The tremendous developments in the field of (bio) medical imaging that have revolutionized modern medicine have opened a new niche for technologies that enable the collection of information above and beyond anatomical, metabolic, and functional information. Our lab has been focusing on the development of one such technology, which is based on genetically encoded systems that can generate MRI contrast from specific cellular and molecular events. These are genes–synthetic, semi–synthetic, and adopted from other organisms that we introduced into the cell's genome. These genes, once expressed, can be used for numerous applications. Here, we will demonstrate how such genes can be used to monitor:
While most of the studies were performed in live rodents, we have recently demonstrated the feasibility of these technologies in pigs, using clinical MRI scanners. Our research is a part of an on-going effort to expand the toolkit of MRI technologies for more comprehensive diagnostics.
Date: March 30th at noon
Department of Radiology
Leiden University Medical Center
The Netherlands
One of the main challenges in MR operation at high fields is to acquire images when the dimensions of the body section being imaged are comparable to the RF wavelength. The resulting RF interferences within the body can severely reduce diagnostic image quality. However, the underlying electromagnetic interactions also raise the question of whether these mechanisms may be exploited to improve performance. This approach, termed "Dielectric Shimming," is a very simple method which allows for adjusting the radiofrequency (RF) fields in high field MR. Previous work has shown that this can improve MR operation in various body applications at 3T as well as neuro applications at 7T. Currently, numerical methods are being developed to harness and exploit this approach.
Date: March 25th at noon
Associate Professor
Universidad Complutense de Madrid
In this overview presentation I will describe from a personal—and necessarily biased—point of view a few aspects of TIME which I have dealt with during my research and teaching on gravity and quantum theory. They encompass various contexts: from Newtonian mechanics and general relativity to quantum gravity and the microscopic structure of spacetime.
Luis J. Garay is an Associate Professor at the Universidad Complutense de Madrid. His area of research is classical and quantum gravity. In particular, he has worked on black holes, quantum fields in curved backgrounds, Hawking radiation, analog models of gravity, emergent gravity, acoustic black holes in Bose-Einstein condensates, quantum gravity and cosmology, and relativistic quantum information.
Date: March 23rd at 2:00 p.m.
Associate Professor
Graduate Program in Electrical and Computer Engineering
Federal University of Technology - Paraná (UTFPR), Brazil
Visiting Scholar
Computer Science
Graduate Center, City University of New York (CUNY)
Compressive sampling/compressed sensing (CS) has shown that it is possible to perfectly reconstruct non-bandlimited signals sampled well below the Nyquist rate. Magnetic Resonance Imaging (MRI) is one of the applications that has benefited from this theory. Sparsifying operators that are effective for real-valued images, such as finite difference and wavelet transform, also work well for complex-valued MRI when phase variations are small. As phase variations increase, even if the phase is smooth, the sparsifying ability of these operators for complex-valued images is reduced. If the phase is known, it is possible to remove it from the complex-valued image before applying the sparsifying operator. Another alternative is to use the sparsifying operator on the magnitude of the image, and use a different operator for the phase, i.e., one related to a smoothness enforcing prior. The proposed method separates the priors for the magnitude and for the phase, in order to improve the applicability of CS to MRI. An improved version of previous approaches, by ourselves and other authors, is proposed to reduce computational cost and enhance the quality of the reconstructed complex-valued MR images with smooth phase. The proposed method utilizes L1 penalty for the transformed magnitude, and a modified L2 penalty for phase, together with a non-linear conjugated gradient optimization. Also, this paper provides an extensive set of experiments to understand the behavior of previous methods and the new approach.
Date: March 22nd at noon
Graduate Student
Biomedical Imaging Program
Sackler Institute, Radiology Department
NYU School of Medicine
Radiofrequency(RF) Coil designs motivated by the ideal current patterns corresponding to the Ultimate Intrinsic SNR (UISNR) have been used to boost central SNR at 3T and 7T for MR imaging. For a cylindrical phantom and a current distribution defined on a concentric cylindrical surface, the ideal current pattern for optimal central SNR includes both divergence-free and curl-free components. At low field, divergence-free current patterns saturate the UISNR and arrays with an increasing number of loops can approach the UISNR. While loops are exclusively divergence-free, recent work has shown that electric dipole antennas include both divergence-free and curl-free current components. To shorten a dipole compared to its self-resonant l/2 length it is necessary to incorporate inductors, which are lossy. In this talk I will present that arrays with an increasing number of electric dipole antennas can approach UISNR for all currents in the center of a head-sized phantom at 7T despite these losses.
Date: March 16th at 2:00 p.m.
Chief Editor
Nature Biomedical Engineering
Launching in January 2017, Nature Biomedical Engineering will publish original research, reviews and commentary of high significance to the biomedical engineering community, including bench scientists interested in devising materials, methods, technologies or therapies to understand or combat disease; engineers designing or optimizing medical devices and procedures; and clinicians leveraging research outputs in biomedical engineering to assess patient health or deliver therapy across a variety of clinical settings and healthcare contexts. In this discussion, the Chief Editor will welcome suggestions about what the journal could do for your field and for the broader biomedical engineering community.
Pep is leading the editorial team of Nature Biomedical Engineering. He has been an editor for Nature Materials for more than 5 years, where he championed the biomaterials content, handling manuscripts and commissioning articles in a wide variety of subjects, including tissue engineering, medical imaging, regenerative medicine, cancer therapy and diagnostics. Previously, Pep conducted research in computational soft matter and biophysics at Columbia University's Chemistry Department in New York City, at the Max Planck Institute of Colloids and Interfaces in Potsdam, and at the Atomic and Molecular Physics Institute in Amsterdam. Pep obtained a PhD in Chemical Engineering in December 2003 from Rovira i Virgili University in Catalonia, Spain.
Date: March 15th at noon
Research Associate
Harvard University
Most tools that scientists use for the preparation of scholarly manuscripts, such as Microsoft Word and LaTeX, function offline and do not account for the born-digital nature of research objects. Also, most authoring tools in use today are not designed for collaboration and as scientific collaborations grow in size, research transparency and the attribution of scholarly credit are at stake. In this talk, I will show how Authorea allows scientists to collaboratively write rich data-driven manuscripts on the web–articles that would natively offer readers a dynamic, interactive experience with an article’s full text, images, data, and code–paving the road to increased data sharing, data reuse, research reproducibility, and Open Science.
Alberto Pepe is the co-founder of Authorea. He recently finished a Postdoctorate in Astrophysics at Harvard University. During his postdoctorate, Alberto was also a fellow of the Berkman Center for Internet and Society and the Institute for Quantitative Social Science. Alberto is the author of 30 publications in the fields of Information Science, Data Science, Computational Social Science, and Astrophysics. He obtained his Ph.D. in Information Science from the University of California, Los Angeles with a dissertation on scientific collaboration networks which was awarded with the Best Dissertation Award by the American Society for Information Science and Technology (ASIS&T). Prior to starting his Ph.D., Alberto worked in the Information Technology Department of CERN, in Geneva, Switzerland, where he worked on data repository software and also promoted Open Access among particle physicists. Alberto holds a M.Sc. in Computer Science and a B.Sc. in Astrophysics, both from University College London, U.K. Alberto was born and raised in the wine-making town of Manduria, in Puglia, Southern Italy.
Date: February 23rd at noon
Graduate Student
Biomedical Imaging Program
Sackler Institute, Radiology Department
NYU School of Medicine
High permittivity, low conductivity materials (HPMs) placed between RF coils and a subject can be used to passively vary the spatial distribution of electric and magnetic fields, independent of or in combination with RF shimming or parallel transmission. This field redistribution has the potential to improve both receive sensitivity and transmit efficiency, and therefore HPMs have the potential to greatly benefit transmit-receive coil array design. In this talk I will present a method for determining the optimal relative permittivity and placement of HPMs close to a transmit array, and the practical restrictions that come along with placing materials with very high permittivities close to resonant loops.
Gillian Haemer received her Bachelors in Biomedical (Electrical) Engineering from the University of Southern California. During her time in Los Angeles, she discovered medical imaging research working as a research assistant on CTA/SPECT data registration at Cedars Sinai Medical Center. She then completed her Masters at the joint program in Biomedical Engineering and Medical Imaging at the University of Tennessee and the University of Memphis, with the design and development of a prototype variable-resolution x-ray breast CT scanner. She is currently a PhD student at the NYU School of Medicine, where she works under the direction of Daniel K. Sodickson and Graham C. Wiggins on MRI hardware engineering challenges at ultra high field strengths.
Date: February 19th at noon
Postdoctoral Fellow
ETH Zurich
Small-angle X–ray scattering (SAXS) occurs when part of the X–ray beam that probes a sample is scattered at small angles, due to differences in electron density distributions within the sample. Moreover, it gives a particularly strong signal in the presence of ordered and periodic systems, that act like slits. Recently, we developed two techniques based on SAXS that can reconstruct the 3D organization of tissue microstructure. In the first technique, called 3D scanning SAXS, local 3D tissue anisotropy is derived by scanning thin sections at different rotation angles. In the second technique, called small–angle scattering tensor tomography, a non–destructive method to reconstruct local anisotropy is introduced by the use of a second sample rotation axis and an iterative reconstruction algorithm based on spherical harmonics. Small-angle scattering tensor tomography extends the concept of traditional tomography: it reconstructs not only scalar values, but multiple parameters per voxel, providing a 3D representation of local material anisotropy. These methods were demonstrated for reconstructing the orientation of the mineralized collagen fibrils in bone trabeculae. Similar experiments can also be performed in other tissues and materials which exhibit structural anisotropy, such as the human brain.
Marios Georgiadis received his Mechanical Engineering diploma from the National Technical University of Athens, Greece. He did his Masters studies in Biomedical Engineering at ETH Zurich, Switzerland, where his thesis “Microfluidic probe for tissue staining in advanced pathology” was awarded the ETH medal. In his PhD at the Institute for Biomechanics of ETH Zurich he developed methods for investigating local tissue anisotropy using X-ray scattering, and applied that to investigate local tissue anisotropy of human trabecular bone. He was runner-up for the Student Award of the European Society of Biomechanics in 2015. He is currently at the Institute for Biomedical Engineering of ETH Zurich and the University of Zurich, where he will be looking at the microstructural anisotropy of brain tissue using X-ray scattering and diffusion MRI.
Date: February 9th at noon
Graduate Student
Biomedical Imaging (BIO) Program
Sackler Institute, Radiology Department
NYU School of Medicine
Diffusion of water molecules is directly influenced by the biological tissue architecture at the micrometer length scale. Capturing this effect using Diffusion MRI has led to the development of numerous applications including early detection of stroke and cancer. However, despite the overwhelming tissue complexity, important non-Gaussian nuances of the diffusion signal are ignored. I have been focused on using time-dependent diffusion as a probe for non-Gaussian behavior within the muscle and prostate. This presents a unique challenge, as acquisition techniques such as STEAM, PGSE, and OGSE, must be tailored to the tissue of interest. In this talk, I will discuss the clinical motivation for pursuing time-dependent diffusion as well as advances in diffusion modeling and acquisition.
Greg immigrated from the city of Tomsk, Russia to Brooklyn, NY in 1994, where he grew up and attended school in Sheepshead Bay. He attended NYU as an undergrad, where he majored in Physics and followed the premed track. However, after numerous experiences working with and shadowing both scientists and clinicians he concluded that a PhD in MRI physics was a suitable middle ground between the two disciplines. He has since been working closely with Dmitry S. Novikov and Els Fieremans on development of Time-Dependent diffusion applications in the body.
Date: February 5th at 1:00 p.m.
Assistant Professor
Department of Computer Science
New York University
Deep learning has become one of the hottest topic in machine learning research in recent years. It began with the 2012 breakthrough in computer vision, the breakthrough that essentially transformed the whole field of computer vision. This breakthrough was followed by those in automatic speech recognition, natural language processing and machine translation. Beyond these recent success stories, deep learning promises much more especially in the areas of multimodal, multitask learning and sequential decision making. In this talk, I will start with a high-level overview on deep learning and discuss these future promises and challenges.
Date: February 3rd at noon
Research Fellow
Department of Radiology
Mayo Clinic
The rising public concerns on CT radiation dose have greatly motivated the development of dose-reducing reconstruction algorithms. However, the development of a reconstruction algorithm is challenged by several aspects. First, collecting CT projection data (i.e., raw materials for CT reconstruction) is challenging: The CT projection data acquired on commercial CT scanners are proprietary and vendor-specific, and therefore not accessible to researchers who do not have research agreements with the vendor. Second, optimizing the algorithm is challenging: Any optimization needs to use diagnostic accuracy as the end goal, but the assessment of diagnostic accuracy via reader studies is time-consuming. Last but not least, validating the algorithm via clinical trials is challenging: The process can be very expensive and labor-intensive. In this talk, I will discuss solutions to these three challenges using examples from my current research: A library of patient projection data in an open and standardized format; a mathematical model that predicts the detection performance of human observers based on the image quality, the viewing condition, and the lesion characteristic; and a computer program that creates positive cases for clinical trials by inserting lesion of known characteristics into images of healthy patients. The same framework not only facilities the development of CT reconstruction algorithms, but can also be adapted by clinical practices (such as the optimization of clinical protocols) to improve diagnostic performance.
Date: January 26th at noon
Associate Professor
University of Massachusetts Medical School
Worcester, MA
The last two-decades have seen an explosion of technology to improve upon the safety and efficacy of brain aneurysm treatment. Despite remarkable improvements in treatment modalities, risk of severe neurological morbidity varies between 5 and 15% of patients with treated unruptured aneurysms. In parallel, increased access to noninvasive neuroimaging has led to a historically unprecedented detection rate of unruptured brain aneurysms. Although the risk of aneurysm rupture is often quite low, the consequences of aneurysmal subarachnoid hemorrhage are devastating with approximately half of the patients not surviving the rupture. Therefore, an approach enabling appropriate selection of patients who would benefit from treatment is urgently needed. Currently, best evidence indicates that size, ethnicity (Finnish, Japanese, or other), location, prior history of subarachnoid hemorrhage, and hypertension should all be considered. Other potential factors elevating rupture risk are family history of subarachnoid hemorrhage, cigarette smoking, and aneurysm morphology. However, given the uncertainty of aneurysm pathophysiology in the progression toward rupture, the precise model to accurately predict aneurysm rupture risk remains elusive. Over the last decade, a plethora of data from human brain aneurysm specimens as well as animal models of intracranial aneurysms has highlighted the role of aneurysm wall inflammation in mural destabilization. Our leading hypothesis is that stable aneurysms can become active, and hence undergo a process of remodeling that involves the invasion of immune cells. This invasion and pursuant inflammation precedes the breakdown of the structural components of the aneurysm wall. Capitalizing on models of vulnerable plaque, we have focused our efforts on in vivo imaging to detect active myeloperoxidase (MPO) in brain aneurysms as a precursor to structural destabilization. We have identified that human brain aneurysms that contain MPO have a statistically higher estimated 5-year rupture risk. Logistic regression modeling of 5-year aneurysm rupture risk and irregular aneurysm morphology when coupled are strong predictors of histologically confirmed MPO presence. Taken together, these data on human brain aneurysm specimens indicate the potential role of MPO as a biomarker for aneurysm instability. In parallel, MR probes have been tested in both animal models of inflamed aneurysms as well as using a unique micro-MRI approach to imaging human brain aneurysm specimens to quantify MPO presence. In summary, MPO detection by MRI may provide clinicians critical information on aneurysm wall biology to make informed decisions regarding treatment.
Date: January 14th at noon
Doctoral Candidate
Senior Associate Researcher
Mount Sinai Medical Center
With the recent introduction of simultaneous PET/MR imaging, various opportunities exist to utilize the co-acquired MRI data to improve the quantitative accuracy of the PET component of the scanner. In this presentation, I will introduce several MR-guided methods for PET attenuation and motion correction focusing on cardiovascular and liver imaging applications.
Date: January 13th at noon
Department of Radiology
University Hospitals Leuven
Digital breast tomosynthesis is a recent imaging modality gaining acceptance as a valuable diagnostic tool. Clinical evaluations have shown that when combined with digital mammography it improves diagnostic accuracy and reduces recall rates. When looking closer at the different lesion types, evidence points to improved visualization of masses and distortions, but potentially worse visualization of microcalcification clusters. Therefore, we improved the visualization of these microcalcifications by expanding the forward model of the maximum likelihood for transmission tomography reconstruction to include an exposure-specific resolution model and modified the update sequence to obtain faster convergence. Concurrently, we developed a channelized Hotelling observer that can predict human observer performance when evaluating detectability of microcalcification targets in a structured phantom background.
Koen Michielsen obtained the degree of Bachelor of Science at the University of Hasselt in 2003, and continued his education at KU Leuven where he graduated cum laude in 2005 with the degree of Master of Science in Physics, with a thesis on "Determining the time delays of lensed quasar J1155+635 from a series of CCD images." After receiving a second Master’s degree in 2007, this time in the field of Medical Radiation Physics and with a thesis titled "Automated data collection strategies and results for patient dosimetry in mammography," he started working as a certified medical physics expert at the Department of Radiology of the University Hospitals in Leuven. He worked there until December 2010, when he started his PhD project at the Department of Imaging and Pathology at KU Leuven on the topic "Maximum a Posteriori Reconstruction of Limited Angle Tomography."
Date: January 12th at noon
Doctoral Candidate
Research Assistant
City University of New York
Characterizing the most relevant geometric structure of complex systems with a single transport measurement is central in many fields. Such characterization of the underlying structural complexity may contribute to early detection of cerebral ischemia, optimization of oil production from rock formations, or characterization of complex biological networks such as protein–interaction networks. The increased structural complexity of such systems—or disorder—makes the establishment of relations between dynamic parameters, such as the diffusion coefficient, and the underlying geometric structure a challenging problem. Disorder may be categorized in a "handful" of universality classes which lead to distinct long-time power law behaviors of the diffusion coefficient, characterized by the dynamical exponent θ. In this seminar, an introduction of the theory of classical transport in disordered media will be discussed. A direct experimental validation of the universal scaling of the diffusion coefficient of H2O diffusing through a homemade phantom of polycarbonate permeable films in a well-defined geometry will be presented. In addition, structural parameters such as the diffusive permeability and structural disorder class of the phantom are experimentally determined. Other topics of the seminar include Pulsed Field Gradient NMR techniques and NMR probe development techniques for spin diffusion measurements.
Antonios received his B.S. in Physics from the University of Ioannina, Greece, and is currently a Ph.D. candidate at the physics department of the City University of New York, The Graduate Center under the supervision of Gregory Boutis. His Ph.D. thesis focuses on classical transport in disordered systems. During his Ph.D., he also collaborated with Ravinath Kausik and Yi-Qiao Song (Schlumberger Doll Research-Boston) working on methane gas adsorption in disordered media. He is also interested in the statistical mechanics of complex networks (collaboration with Hernan Makse-CUNY).
Date: January 8th at noon
Associate Professor
Oregon Health & Science University
The efficacy of therapeutic interventions for neurodevelopmental disorders improves when the disorder is detected early in central nervous system development. We have developed MRI strategies for characterizing neural maturation in the fetal cerebral cortex and for monitoring placental function throughout the second half of the gestational period. To assess the sensitivity of these fetal MRI methods, we have developed a nonhuman primate model of fetal alcohol spectrum disorders. In this context, we demonstrate the utility of MRI for precise characterization of perturbations to normal fetal development.
Dr. Kroenke's research group focuses on developing MRI strategies for characterizing the biological bases of neurodevelopmental disorders. Dr. Kroenke received his PhD in molecular biophysics and biochemistry at Columbia University. He then completed postdoctoral studies in the Washington University Department of Radiology. Dr. Kroenke is currently Associate Professor of Behavioral Neuroscience and Associate Scientist in the Oregon Health & Science University Advanced Imaging Research Center and Oregon National Primate Research Center.
Date: December 17th at noon
Professor of Radiology
Stanford University
Vice President, ISMRM
Date: December 15th at noon
Division of Cancer Imaging Research
Russell H. Morgan Department of Radiology and Radiological Science
The Sidney Kimmel Comprehensive Cancer Center
The Johns Hopkins University School of Medicine
Angiogenesis or new blood vessel formation is one of the ‘hallmarks’ of cancer and necessary for tumor progression and metastasis. However, tumor blood vessels are structurally and functionally abnormal compared to vessels in healthy tissue. These abnormalities profoundly affect tumor hemodynamics, metastatic potential, and drug delivery. A recent explosion in imaging technologies has revolutionized our understanding of the role of the tumor vasculature and these phenomena. This lecture will highlight new 3D imaging techniques for visualizing the tumor vasculature; strategies for imaging the vascular phenotype at different spatial scales; and describe how 3D imaging data that quantify tissue morphology and molecular factors can be used in computational models of cancer and image contrast. The integration of preclinical cancer imaging data lays the groundwork for systems biologists to map the ‘vasculome’ of a wide array of diseases. Mapping the tumor vasculature using multiscale imaging and modeling also enhances our understanding of the tumor microenvironment. Collectively, these advances enable us to relate the genotype to the vascular phenotype, identify novel drug targets, and develop reliable clinical biomarkers of cancer.
Arvind P. Pathak received the BS in Electronics Engineering from the University of Poona, India. He received his PhD from the joint program in Functional Imaging between the Biophysics Department at the Medical College of Wisconsin and the department of Biomedical Engineering at Marquette University, Milwaukee, Wisconsin. During his PhD, he was a Whitaker Foundation Fellow. He completed his postdoctoral fellowship at the Johns Hopkins University School of Medicine in the Molecular Imaging Program. He then joined the faculty of the Departments of Radiology and Oncology at Johns Hopkins. His cancer imaging research has been recognized by numerous journal covers and awards including the Bill Negendank Award from the International Society for Magnetic Resonance in Medicine (ISMRM) given to “outstanding young investigators in cancer MRI” and the Career Catalyst Award from the Susan Komen Foundation.
Date: December 14th at noon
Malcolm B. Hanson Professor of Radiology
Center for Magnetic Resonance Research and Department of Radiology
University of Minnesota
Date: December 4th at noon
Professor of Radiology and Biomedical Imaging and of Neurosurgery
Director of MRI Research
Yale University
This presentation will discuss the uniqueness of individual functional connectivity profiles and how these signatures reflect behavior. The connectivity measures reflect underlying intrinsic connections that are modified only slightly with different task or resting-state conditions. A method for relating connectivity profiles to behavior, building a model, and then testing the predictive capabilities of the model will be shown. It will be shown that the areas that most characterize individual identification include frontal and parietal circuits. It will also be shown that specific connectivity patterns reflect measures of fluid intelligence and attention.
Date: November 25th at noon
Doctoral Candidate
Karolinska Institutet
Stockholm
Cardiac cine imaging is an important part of the clinical cardiac exam today, used for assessment of wall function and ventricular volume measurements. However, this is still mostly done with a stack of 2D images, each acquired during a breath-hold to avoid motion artifacts from the respiration. This method is both time consuming, inflexible and gives rise to many artifacts from poor or inconsistent breath holding. The desirable solution is a 3D free breathing technique which minimizes patient cooperation and gives high flexibility after acquisition for extracting arbitrary slice positions from the whole heart. This talk will focus on fast 3D k-space acquisition and reconstruction and the specific requirements trajectories need in order to cope with the constant motion of the beating heart and respiration. Furthermore, examples of different respiratory self-gating techniques will be shown and discussed. Finally, some preliminary results from our suggestion of combining a 3D acquisition technique and self-gating method will be given.
Karen Holst is a PhD student at Karolinska Institutet, Stockholm. She received her MSc in biomedical engineering at Technical University of Denmark where she specialized in medical imaging and radiation physics. Her thesis work is focused on free-breathing ventricular volumetric imaging resolved over both the cardiac and the respiratory cycles with magnetic resonance imaging.
Date: November 24th at noon
Doctoral Candidate
Medical Physics
University of Chicago
Recent advances in x-ray computed tomography (CT) have led to a new imaging paradigm, called “spectral CT,” whereby a plurality of unique energy measurements are acquired, nearly simultaneously, in a single scan. It has been shown that this extra spectral information can be used to determine the entire energy dependence of the x-ray attenuation coefficient. This leads to the elimination of common image artifacts (e.g. beam hardening), a reduction in radiation dose, and improved quantification of contrast materials. In this talk, I will give a brief overview of spectral CT theory and clinical applications. Then, I will discuss how task-based, mathematical observer models and image reconstruction algorithms can be generalized to accommodate this extra “spectral” dimension. The primary goal of my research is to improve the accuracy and robustness of spectral CT imaging by (1) developing objective metrics for optimizing imaging parameters and hardware design and (2) combining optimization-based reconstruction methods with sparsity exploiting image priors, tailored to multispectral data. I will discuss some applications of these techniques to novel geometries with challenging data conditions.
David Rigie is a Ph.D. candidate in Medical Physics at the University of Chicago. Prior to arriving in Chicago, he studied Applied Physics at Cornell, with a concentration in molecular biophysics. His research interests include model-based image reconstruction, physical modeling, and spectral x-ray CT. He is currently involved in a collaboration with Toshiba Medical Research Institute, USA investigating the use of energy-resolving, photon-counting detectors for diagnostic CT.
Date: October 27th at noon
Professor and Vice Chair for Research
Department of Anesthesiology
Stony Brook School of Medicine
Date: October 20th at noon
Richman Family Professor of Alzheimer’s and Related Diseases
Department of Psychiatry and Behavioral Sciences
Johns Hopkins University School of Medicine
Abstract: Neuropsychiatric symptoms in late life are a major predictor of cognitive decline and the dementia transition. The pathophysiology underlying these symptoms is poorly understood. Over the past decade, advances in positron emission tomography (PET) instrumentation and radiotracer chemistry have provided an unprecedented opportunity to test mechanistic hypotheses generated from human post-mortem data and transgenic Alzheimer mouse models. Studies have been performed to identify the neural circuitry of affective and cognitive symptoms in late life depression and the role of the serotonin system. Building upon this work, multi-radiotracer PET imaging studies have been performed in late-life depression and mild cognitive impairment. These studies have tested the observation based on transgenic amyloid mouse models; of vulnerability of cortical monoamine projections (serotonin, to a greater extent) may precede beta-amyloid deposition. Understanding the pathophysiology of late life depression and neuropsychiatric symptoms and targeting these symptoms may represent a strategy for earlier intervention and prevention.
Date: October 19th at noon
Doctoral Candidate
Biomedical Engineering Department
University of Michigan
In recent years there has been a growing interest in accelerating MRI scans, both from the viewpoint of reducing the computation time necessary to produce images as well as reducing the amount of time the patient spends in the scanner. This presentation will discuss both of these aspects of MRI acceleration. The first will be the development of a fast algorithm for the setting when parallel receive coils are used in conjunction with compressed sensing assumptions to reduce the scan time. This requires solving a complicated optimization problem, which can take more time than the scan itself. I will discuss an algorithm, BARISTA, that significantly reduces this computation time relative to state-of-the-art methods by carefully considering the structure of the sensitivity maps from the parallel receive coils. For the second aspect of MRI acceleration, I will discuss recent advances for the estimation of functional MRI time series of images using low rank modelling. The low rank modelling approach is demonstrated to be effective in simulation results relative to standard acquisition methods, and preliminary results using prospectively undersampled data will also be shown.
Matthew Muckley is a Ph.D. candidate in the biomedical engineering department at the University of Michigan. Matthew started his academic career by earning his B.S. at Purdue University, where he graduated With Distinction. Matthew's research at Michigan focuses on the application of signal processing methods to various imaging modalities, including MRI, X-Ray CT, optical imaging, and atomic force microscopy. For his research, Matthew has been awarded first place in a KLA-Tencor Image Processing contest, a Rollin M. Gerstacker Foundation Fellowship, a GAANN Fellowship, and a Rackham Predoctoral Fellowship. Aside from his research endeavors, Matthew also actively participates in the Biomedical Engineering Graduate Student Council at Michigan, for which he has served as President for the last year.
Professor of Rehabilitation Medicine and Psychiatry
Director of Psychology, Rusk Rehabilitation
NYU Langone Medical Center
Advances in neuroimaging have clearly changed the way that brain function, dysfunction, and rehabilitation may be conceptualized and studied. In addition to complementing existing behavioral and psychometric data, neuroimaging technologies such as fMRI and DTI novel inferences that cannot necessarily be made through other approach to studying brain-behavior relationships. This talk will provide an overview of contemporary functional neuroimaging techniques that have been specifically applied to traumatic brain injury in humans. The evidence-base (and often the lack thereof) of some technologies will be discussed in relation to the clinical appropriateness of these technologies. Finally, future research issues will be addressed through discussion of methodological and technical concerns rehabilitation, as well as how the integration of functional neuroimaging and clinical neuropsychology may inform the assessment and rehabilitation process.
Dr. Ricker is a board certified neuropsychologist and rehabilitation psychologist who came to NYU in 2013 as Professor of Rehabilitation Medicine and Director of Psychology for Rusk Rehabilitation at NYU Langone Medical Center. Prior to coming to NYU, he was a tenured Associate Professor and Vice Chair for Neuropsychology & Rehabilitation Psychology in the Department of Physical Medicine & Rehabilitation at the University of Pittsburgh School of Medicine. Dr. Ricker’s research career has been devoted to the study of cognitive impairment, recovery, and rehabilitation following human traumatic brain injury (TBI). He was among the very first investigators in the late 1990s to apply functional neuroimaging to investigate cognition after TBI. His work was honored in 2001 by two separate early career awards from the American Psychological Association, one in clinical neuropsychology and the other in rehabilitation psychology. Dr. Ricker’s current research focuses on the application of anatomic, functional, connectomic, and molecular brain imaging technologies in the investigation of neuropsychological impairment after brain injury. His NIH-funded research has included the use of technologies such as functional MRI, positron emission tomography, diffusion tensor imaging after TBI. He is a member of the editorial boards of four research journals (Journal of Clinical & Experimental Neuropsychology; Journal of Head Trauma Rehabilitation; Clinical Neuropsychologist; and, Rehabilitation Psychology), and serves as a grant reviewer for several U.S. and Canadian agencies, including the National Institutes of Health, the Centers for Disease Control and Injury Prevention, the Department of Veterans Affairs, and the Ontario Neurotrauma Foundation.
Postdoctoral Researcher
Bernard & Irene Schwartz Center for Biomedical Imaging
NYU Langone Medical Center
Steady-state sequences are a class of rapid imaging techniques based on gradient-echo acquisitions with short repetition times. This class includes the balanced Steady-State Free Precession (bSSFP, TrueFISP) sequence, which provides the highest signal-to-noise ratio per unit time among all known imaging sequences. However, aside from a few applications such as cardiac imaging, this method is hardly established in the clinical routine. The main reasons are banding artifacts, which are signal voids due to magnetic field inhomogeneities, and the obtained T2/T1-weighted mixed contrast. In this talk, two novel techniques will be presented, which overcome these limitations and could allow for a more widespread use of bSSFP for MR diagnostics.
Dr. Benkert is a postdoctoral researcher at CBI working under the supervision of Dr. Block. During his undergraduate studies he studied to become a teacher for Physics and Mathematics in Wuerzburg, Germany and then focused on MRI, writing his thesis on “Quantification of Relaxation Times in MRI with Steady-State sequences”. During his PhD in Wuerzburg, he continued his MRI work under the supervision of Dr. Felix Breuer. The title of his dissertation was “Novel Steady-State Techniques for Magnetic Resonance Imaging”. His PhD work represents the topic of his Research Forum talk. His current research focuses on further developments for GRASP using fat-water separation.
Department of Radiology
Michigan State University
The microvasculature is critical for the control of blood flow and tissue perfusion. Compromised microvascular function occurs during aging as well as several disease states and may contribute to compromised muscle performance within these populations. Muscle fMRI using blood-oxygen level-dependent imaging (BOLD) allows noninvasive assessment of peripheral microvascular function. Our findings show age-related reductions in lower extremity BOLD and enhancement of muscle BOLD with exercise training in older adults.
Postdoctoral Fellow
KU Leuven
Belgium
Regularized iterative image reconstruction is used in Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI). In combined PET/MRI acquisitions, PET and MR images both suffer from artifacts due to acquisition time constraints. Since these artifacts are fundamentally different, we would like to investigate whether joint iterative reconstruction using joint prior information could improve the image quality in both modalities. In this presentation, Georg Schramm will give a short overview of initial results from simulations with different joint priors.
Postdoctoral Fellow
Bernard & Irene Schwartz Center for Biomedical Imaging
NYU Langone Medical Center
Dissolution dynamic nuclear polarization enables real-time non-invasive measurement of metabolic fluxes using magnetic resonance spectroscopy. Quantitative kinetic information of in vivo metabolism is of great interest for medicine as a key characteristic of some diseases, i.e. tumors. In this talk, the developed comprehensive methods for the data acquisition, quantification, interpretation, and visualization of dynamic 13C metabolite signals in vitro and in vivo will be presented on the example of hyperpolarized [1-13C]pyruvate.
Postdoctoral Research Fellow
Department of Psychiatry
Columbia University
Functional magnetic resonance imaging/spectroscopy (fMRI/MRS) have been used to visualize abnormalities in unipolar depression (MDD) with mixed results. Patients with medication refractory depression (TRD) represent almost one-third of all patients with MDD. There are relatively few treatment options for these patients. Prefrontal repetitive transcranial magnetic stimulation (rTMS) is a non-invasive, well-tolerated alternative technique to pharmacological treatment for MDD. TMS induces stronger electric currents in superficial regions than in deeper structures. However, TMS can modify ongoing neuronal activity within complex neuronal circuits. Effects of TMS can propagate beyond the site of stimulation, impacting a distributed network of brain regions. These observations suggest that TMS may relieve depression by modulating synaptic strength both locally and at distant sites modulating functional connectivity in cortical networks. Some evidence for TMS as an antidepressant points toward cortical excitability increases to normalize abnormal levels of activity and distributed modulation of brain activity resulting in network-specific release of neurotransmitters and activity modulation. However, it remains unclear how TMS targeted to Dorsolateral Prefrontal Cortex (DLPFC) exerts its antidepressant effect. The future of TMS relies on identifying its mechanisms of action across the brain. The combination of TMS and BOLD fMRI or MR spectroscopy at lower field strength have shown to be promising. However, the resolution of fMRI and MRSI at lower fields are too low for depiction of node size and temporal resolution. These shortcomings of low field MRI prevent detection of default mode networks (DMN) function with appropriate representation of the strength of FC between the nodes. We have used 7T functional connectivity and MRSI at 0.5cc resolution to demonstrate correlation between glutamate concentration and DMN function. We will discuss how 7T is crucial in visualizing DMN in various brain regions implicated in MDD. A discussion will be presented of technical challenges in realizing 7T advantages in order to use the combined fMRI/MRSI in search for faulty networks. Eliminating 7T RF coil and B0 inhomogeneity in the skull base will allow comparing fMRI/MRSI of normal subjects and patients with MDD which, in turn, could make diagnosis and treatment of these patients a quantitative practice. Such tools will reveal further understanding into the impact of TMS on brain function.
PhD Candidate
University Medical Center Utrecht
Diffusion MRI (dMRI) has offered exciting new avenues for investigating microstructural and architectural characteristics of tissue in vivo. The growing interest for integrating dMRI in many clinical and scientific studies has triggered the development of different strategies to process dMRI data. These developments include, amongst others, modeling and reconstruction of the dMRI signal beyond diffusion tensor imaging (DTI), and new ways to extract information on the (local) geometry of fiber tractography streamlines. This presentation will focus on our recent work in this area, including robust fitting in diffusion kurtosis imaging (DKI), calibrating the response function for spherical deconvolution (SD), the acquisition of a reference dataset to test processing pipelines, and quantifying whether streamlines locally form a grid-like pattern.
PhD Candidate
Universite de Sherbrooke
In the past decade, the fusion between diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) has opened the way for exploring structure-function relationships in-vivo. As it stands, the common approach usually consists of analysing fMRI and dMRI datasets separately or using one to inform the other, such as using fMRI activation sites to reconstruct dMRI streamlines that interconnect them. Also, given the large inter-individual variability of the healthy human brain, it is possible that valuable information is lost when a fixed set of dMRI/fMRI analysis parameters such as threshold values are assumed constant across subjects. By allowing one to modify such parameters while viewing the results in real-time, one can begin to fully explore the sensitivity of structure-function relations and how they differ across brain areas and individuals. This is especially important when interpreting how structure-function relationships are altered in patients with neurological disorders, such as the presence of a tumor. In this study, we present and validate a novel approach to achieve such visualization: First, we present an interactive method to generate and visualize tractography-driven resting-state functional connectivity. Next, we demonstrate how our proposed approach can be used in a neurosurgical planning context. We believe this approach will promote the exploration of structure-function relationships in a subject-specific aspect and will open new opportunities for connectomics.
Research Assistant
Department of Radiology
University Medical Center Freiburg
In this talk I will demonstrate the possibility to form spin echoes after a single excitation pulse, where the time between the end of the pulse and the echo is longer than the length of the pulse itself. This stands in contrast to Hahn's theory spin echoes, where the length of a composite pulse is at least equal to the time between the end of the pulse and the echo. A representative spin echo pulse is implemented in an inversion recovery SNAPSHOT-FLASH sequence in order to retrieve quantitative T1- and proton density maps of the lung with increased signal intensity. Last but not least the theoretical concept in translated to a pseudo steady state free precession sequence for MR-fingerprinting.
Jakob Asslander studied physics in Würzburg (Germany), where he began conducting MRI research. Thereafter, he obtained a PhD under Jürgen Henning in Freiburg (Germany). During doctoral study, Dr. Asslander focused on fast fMRI acquisition techniques with reduced susceptibility to artifacts. More recently, he has pursued research in RF-pulse design and optimal control algorithms.
Assistant Professor
Department of Bio and Brain Engineering
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
Magnetic resonance imaging (MRI) provides anatomical, physiological, and functional information of our body noninvasively. In this seminar, some new approaches to these imaging modalities will be introduced. The new approaches include techniques for (i) acquisition of time-of-flight MR angiogram and blood oxygenation level dependent (BOLD) MR venogram (often called susceptibility-weighted Imaging, SWI) simultaneously with minimal impacts on the image quality to each other, (ii) imaging blood perfusion and magnetization transfer (MT) asymmetry simultaneously with interslice blood flow and MT effects in 2D sequential multi-slice imaging, and (iii) better understanding of signal sources of high-resolution balanced steady-state free precession functional MRI at high field. Applications of compressed sensing algorithms to these imaging methods will be introduced. Images from humans and animals at various field strengths (3T - 9.4T) will be demonstrated and potential applications of these imaging methods for clinical diagnosis will be discussed.
Sung-Hong Park received a PhD in bioengineering from the University of Pittsburgh in 2009.
Date: July 24, 2015 at noon
Professor of Radiology and Biomedical Engineering
Washington University
The talk focuses on PET-MR imaging of atherosclerosis using novel radiopharmaceuticals targeted to specific components suggestive of plaque vulnerability including Natriuretic Peptide Receptor-C and hypoxic macrophages. The speaker will go describe the steps of taking one of these radiotracers from preclinical development and toxicity testing to FDA IND application, approval and clinical trial.
Pamela Woodard is a Professor of Radiology and Biomedical Engineering at Washington University where she is Radiology Vice Chair of Clinical and Translational Research, Director of the Center for Clinical Imaging Research (CCIR) and Head of Advanced Cardiac Imaging. She is past chair of the Cardiovascular Radiology and Intervention (CVRI) Council and of the American Heart Association and is currently a member of the AHA Operations Committee and past president of the North American Society for Cardiovascular Imaging. She has over 140 peer-reviewed publications and has been PI, on the steering committee or co-investigator on multiple NIH-funded grants and clinical trials.
Date: July 21, 2015 at noon
Research Associate
University of Cambridge
The Stejskal-Tanner equation for diffusion tensor imaging (DTI) produces a non-linear correspondence between the DWI measurements and the tensor field. For correct noise modelling, we should in principle include this non-linearity in our DTI reconstruction models. This results in a difficult non-convex optimisation problem. In this talk, I will discuss a primal-dual optimisation method that can effectively handle the Stejskal-Tanner equation—at least when no other complications enter the reconstruction model. In practise, however, our knowledge of the measurement errors and noise is only partial, and accurate noise modelling is not feasible. I will therefore look at the efficacy of foregoing accurate noise modelling, and reducing our knowledge of measurement and model errors and noise to simple bounds, effectively obliterating the Stejskal-Tanner equation from the model (joint work with Yury Korolev and Artur Gorokh).
Tuomo Valkonen received his Ph.D in scientific computing from the University of Jyväskylä (Finland) in 2008. He has since then worked in well-known research groups in Graz, Cambridge and Quito. Currently in Cambridge, his research concentrates on the mathematical analysis of image processing models, towards studying their reliability, and the development of fast optimisation algorithms for the solution of these models.
Date: April 21, 2015 at noon
Medical Physics
Department of Radiology
University Medical Center Freiburg
Dynamic Susceptibility-Contrast MRI can be used to measure the cerebral blood flow in the brain. The method has successfully been applied in clinical routine for over a decade, particularly in Stroke, but it is currently not exploiting its full potential due to several problems concerning the correct quantification. The major problem is related to the measurement of the arterial input function (AIF). The key weakness of the existing, conventional technique is an insufficient consideration of the different physical effects of paramagnetic contrast agent in large blood vessels, and in tissue. In this work, these effects are thoroughly analysed to design an extended measurement sequence with an additional module dedicated to the correct measurement of the blood signal. With this, the AIF can accurately and quantitatively be determined. In a comparison study in the porcine model, the proposed technique is validated against the current gold standard, positron-emission-tomography (PET). This quantitative comparison can for the first time be performed without additional normalisation factors. The results demonstrate a good agreement of both methods. The comparison further reveals that the reasonable interpretation of calculated maps for both, MRI and PET is not straightforward, and requires consideration of the corresponding kinetic models as well as the physics of the tracers used in the different methods.
Dr. Kellner's profile at Universitätsklinikum Freiburg.
Date: March 31st, 2015 at noon
Department of NMR
All India Institute of Medical Sciences
New Delhi, India
The growing interest in systems perspective is not only revolutionising cell biology but also providing the impetus for clinical medicine to shift from a reductionistic to a holistic approach for efficient disease management. This inevitably brings into focus one of the longest unbroken healthcare systems in the world, i.e. ayurveda, indigenous to the Indian subcontinent. The unique ability of NMR to study whole systems (in vitro and in vivo) and generate a wide range of information non-invasively makes it ideally suited to study holistic medicine like ayurveda. It offers a powerful non-invasive means to not only validate ayurveda but also to gain understanding of its concepts and translate them for use in modern healthcare. Different areas ranging from ayurveda’s therapeutic use of medicinal plants to diagnosis, treatment efficacy and concepts of preventive healthcare can be studied and validated effectively through NMR, opening new vistas for expanding the role of NMR in healthcare. This presentation, while outlining the various potential applications of MR in ayurveda will also elaborate on the systems approach of ayurveda.
Dr. Rama Jayasundar, after her initial training in Physics, obtained her PhD in NMR from Cambridge University, UK. In addition to her main training as a physicist, she is also a qualified doctor trained in both ayurveda (the indigenous Indian medical system) and modern medicine. She holds a Bachelor’s degree in Ayurvedic Medicine (BAMS - Bachelor of Ayurvedic Medicine and Surgery). She is currently a faculty in the Department of NMR, All India Institute of Medical Sciences (AIIMS), New Delhi, India. Her area of specialization is Biomedical MR - RF coil designing and building, RF pulse sequence programming, clinical imaging, and spectroscopy. She developed indigenously a low-cost MR coil for clinical use, for which she received the Young Scientist Award. During her stint as a visiting Professor at the Max Planck Institute of Biophysical Chemistry, Gottingen, Germany (1997-1998), she worked on the development of functional MR spectroscopy techniques. She has authored a number of research publications in peer-reviewed journals and has also won many awards and honors. Using her dual qualification as an NMR scientist and a professionally qualified ayurvedic doctor, she is currently involved in scientific research in Ayurveda. Her research interests range from applications of NMR, MRI and other analytical techniques in basic and clinical ayurvedic research.
Date: March 30th, 2015 at noon
Professor and Chairman
Department of Radiology
University Hospital Basel
This talk was initially presented as a plenary during the annual ISMRM meeting in Montreal in 2011. It focuses on the lack of speed and simplicity as well as the lack of robustness of MR imaging in comparison to other cross-sectional imaging modalities. Now, almost 4 years later, this talk will be repeated in its original form to challenge NYU’s research group to answer the simple question – what has changed since?
Dr. Merkle's profile at Universitatsspital Basel.
Date: March 24th, 2014 at 11:00am
Group Leader for Neuroscience
Simons Center for Data Analysis
Simons Foundation, New York City
Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the visual system remains elusive. By developing a semi-automated pipeline using electron microscopy we were able to reconstruct the biggest connectome to-date within the Drosophila visual system and identify neurons and synapses comprising the motion detection circuit motif. Electrophysiological recordings from the identified neurons have confirmed our predictions. More recently, a similar motif has been identified in the vertebrate retina suggesting that the principles of neural computation are shared across species.
Before coming to the Simons Foundation in 2014, Mitya Chklovskii was a group leader at the Howard Hughes Medical Institute’s (HHMI) Janelia Farm Research Campus in Ashburn, Virginia. Chklovskii also initiated and led a collaborative project at HHMI that assembled the largest-ever connectome, a comprehensive map of neural connections in the brain. Before that, he worked at Cold Spring Harbor Laboratory in New York, where he founded the first theoretical neuroscience group, having worked there as a first assistant, and later an associate professor. As group leader for neuroscience, Chklovskii leads an effort to understand how the brain analyzes complex datasets streamed by sensory organs, in an attempt to create artificial neural systems. He holds a Ph.D. in physics from the Massachusetts Institute of Technology.
Date: August 26th, 2014 at 12:00pm
Postdoctoral Fellow
Department of Electrical Engineering
Technion – Israel Institute of Technology, Haifa
Magnetic Resonance Imaging (MRI) is the method of choice for diagnosis, evaluation and follow-up of brain pathologies. In the common treatment scheme, patients are repeatedly scanned every few weeks or months to assess disease progression and treatment response. Although the important information for clinical evaluation lies in the change between the follow-up MRI and the former one, every follow-up scan is acquired anew. This makes most of the data in the later scan redundant. In MRI, data is acquired in a spatial frequency domain, called "k-space". In my talk I'll discuss the application of compressed sensing (CS) for MRI and the mutual similarity of follow-up scans in longitudinal MRI studies. I'll present a sampling and reconstruction framework that exploits the redundancy of the acquired data in longitudinal studies. This would rely on two extensions of compressed sensing, adaptive-CS and weighted-CS. In adaptive CS, k-space sampling locations are optimized such that the acquired data is focused on the change between the follow-up MRI and the former one. Weighted CS uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. Results are presented on MRI scans of patients with brain tumors, and demonstrate improved spatial resolution and accelerated acquisition for 2D and 3D brain imaging at 10-fold k-space undersampling.
Lior Weizman received the B.Sc. and M.Sc. degrees in Electrical Engineering from Ben-Gurion University of the Negev, Beer-Sheva, Israel, in 2002 and 2004, respectively, and the Ph.D. degree in computer science in 2013 from the Hebrew University of Jerusalem, Israel. From 2005 through 2008 he was with RAFAEL, Advanced Defense Systems LTD. During 2011 he was a visiting student at Stanford University, CA. He is currently a post-doctoral fellow at the Department of Electrical Engineering, Technion - Israel Institute of Technology, Haifa. His research interests are in the general areas of sampling theory, statistical signal processing and their applications to medical image processing and medical imaging.
Date: August 25th, 2014 at 12:00pm
Chief, Cerebral Microcirculation Unit
Laboratory of Functional and Molecular Imaging
National Institute of Neurological Disorders and Stroke
National Institutes of Health
Date: August 20th, 2014 at 12:00pm
Professor
Department of Radiation Oncology and Biomedical Engineering
University of Michigan Medical School
Professor
Department of Radiation Oncology, Radiology and Biomedical Engineering
University of Michigan Medical School
Date: August 19th, 2014 at 12:00pm
Professor and Director of Stem Cell Molecular Imaging
Department of Radiology
University of California, San Diego
An unmet challenge to successful clinical development of stem cell therapies is the development of non-invasive methods to image the behavior and movement of cells following transplant into patients. Moreover, imaging is needed to improve safety surveillance of cell therapies to help overcome regulatory hurdles. MRI is experiencing a rapid expansion in its ability to visualize specific cell populations in vivo. These capabilities are facilitated by the development of new imaging probes that tag cells prior to transfer or alter a cell’s proteome to facilitate MRI detection. This talk will first cover a new approach for cell tracking developed in our lab called ‘in vivo cytometry.’ In this approach, cell populations of interest, such as stem cells, are tracked and quantified in vivo. We formulate novel perfluorocarbon (PFC) emulsions to label cells ex vivo. The labeled cells are then introduced into the subject and their migration can be monitored using fluorine-19 (19F) MRI. The 19F images are extremely selective for the labeled cells, with no background signal from the host’s tissues. Moreover, the absolute number of labeled cells in regions of interest can be estimated directly from the in vivo 19F images. Additionally, the PFC emulsion reagents have bio-sensing properties that report on the absolute level of intracellular oxygen and can potentially be used to monitor cell differentiation or apoptosis in vivo. Looking ahead, MRI will be able to harvest the power of molecular biological tools to impart exogenous image contrast to living tissue in a cell-specific or event-related manner. This will be accomplished using transgenic and vector technologies to express reporter genes coding for paramagnetic metalloproteins. Towards this goal, I will describe efforts to develop and characterize new generations of nucleic-acid based MRI reporters that render cells paramagnetic and detectable in vivo. For example, MRI reporters can be used for labeling stem cells for long-term tracking in vivo.
Eric T. Ahrens, Ph.D., is a Professor and Director of Stem Cell Molecular Imaging in the Department of Radiology at the University of California, San Diego. Formally, he was a Professor of Biological Sciences at Carnegie Mellon University and the Director of the Pittsburgh NMR Center for Biomedical Research. Prior to this, he served as a Senior Research Fellow in the Department of Biology at the California Institute of Technology. He holds a Ph.D. in physics from the University of California at Los Angeles and was a graduate fellow at Los Alamos National Laboratory. Ahrens’ research investigates in vivo biological processes using unique molecular, cellular and anatomical MRI and NMR methods.
Date: August 12th, 2014 at 12:00pm
Siemens Healthcare Molecular Imaging
In positron emission tomography (PET), attenuation of the annihilation radiation in the body is the largest physical effect confounding the quantitative interpretation of the emission data. Traditional γ-ray transmission (TX) measurements for attenuation correction in clinical PET were largely abandoned 12 years ago with the advent of PET/CT. Recently, however, several technological developments have converged to significantly enhance the power of TX measurements, sparking renewed interest. These include new joint reconstruction algorithms for simultaneously acquired emission and transmission data; time-of-flight (TOF) measurement capability for discriminating attenuation effects in emission data; and the positron beam technique for injecting transmission sources into the field of view of integrated PET/MR systems. In this talk we will describe a novel solution for PET attenuation correction in the head based on the joint reconstruction of simultaneously acquired emission and sparse transmission (sTX) data corresponding to 20 fixed line sources placed in a ring around the head. Simulations of an 18FDG study show that the sTX data effectively constrain cross-talk. Bone, soft tissue and voids are approximately represented in the estimated attenuation image. The results are compared to a standard MLEM reconstruction of emission-only data, and to joint reconstruction of simultaneous emission-transmission data using a full-ring source. We find that 10 to 20% underestimation of activity in the peripheral regions of the brain in the latter two images is reduced to < 5% on average in the sTX case. We thus demonstrate that an sTX array can provide better cross-talk reduction than a conventional full-ring transmission source, and will offer a qualitative explanation of why this occurs. We will also examine the impact of TOF information on the joint reconstruction in the noise-free case. We estimate that such an sTX technique would increase patient radiation dose in a typical 18FDG clinical study by < 4%.
Dr. Watson earned a PhD in physics from Yale University in 1980. Following post-doctoral study at the California Institute of Technology in planetary science, he joined the corporate research staff of Schlumberger in Ridgefield, Connecticut in 1982, where he developed Monte Carlo simulations of γ-ray and neutron transport for the design and interpretation of nuclear well-logging instruments. In 1993, he joined CTI PET Systems in Knoxville, Tennessee, which subsequently merged into Siemens Healthcare. At CTI/Siemens, Dr. Watson has been involved in nearly all aspects of the physics of PET, PET/CT, and PET/MR scanners. He is the author of a widely used 3D scatter simulation algorithm for the correction of positron emission data. From 1999 to 2002, he was the project leader for the development of the first commercial PET/CT scanner. He served as the chief PET physicist for the development of the first integrated whole-body PET/MR, Siemens’ mMR. His current research interests include applications of positron beams in PET/MR systems, and the development of next-generation transmission systems for the attenuation correction of PET data. He is the author of numerous scientific publications and patents in the field of PET instrumentation, and serves on the editorial board of EJNMMI-Physics.
Date: August 11th, 2014 at 10:00am
Assistant Professor of Radiology
Durham, NC
When the brain is situated in a magnetic field, it creates a small field of its own in response to the presence of the external field. This interaction, though extremely weak, becomes measurable under the strong field provided by MRI scanners. With MRI, this small perturbation field can also be spatially localized and quantified. The strength and direction of the perturbation is influenced by a number of physiologically important factors including molecular composition, cellular organization, and neuronal connectivity. By imaging this field perturbation, one may then be able to infer a wealth of information about brain microstructure. Such information includes, for example, iron deposit in aging and Parkinson’s disease, myelination in brain development, demyelination in multiple sclerosis, and neuronal connectivity. Besides the brain, this magnetic interaction is also significant in many other organs, including the kidney and heart. I will present some recent methodological developments and discuss potential applications.
Date: August 4th, 2014 at 9:30am
Professor Pediatrics and Bioengineering
Adjunct Professor of Radiology
Department of Pediatrics, University of Washington, Seattle, WA
Recent work that combines computer vision with fast MR imaging techniques is beginning to allow the collection of full 3D MRI scans of the human fetal brain in-utero without sedation. The basic ideas behind the engineering approaches to these techniques will be reviewed with examples on typical clinical structural and diffusion imaging studies. Results of the application of these imaging techniques to study human fetal brain development by constructing spatio-temporal growth models will then be covered.
Dr. Studholme is a Professor of Pediatrics and Bioengineering, and Adjunct Professor of Radiology at the University of Washington, Seattle. He completed his Ph.D. in medical physics and biophysics from the University of London and a postdoctoral fellowship in diagnostic radiology at Yale University. Dr. Studholme’s research focuses on the development of new mathematical and computational algorithms to manipulate and analyze biomedical image data. His work is currently motivated by the study of brain anatomy and the patterns of its change over time in two broad clinical areas: fetal and pre-term infant brain development, and neurodegenerative processes in adults.
Date: July 29th, 2014 at 12:00pm
Assistant Professor; Dir Reproductive Health & Benign Disorders of Prostate
Departments of Obstetrics and Gynecology (Obs/Gyn) and Urology (Urology)
NYU Urology Associates
For information on Timothy Duong's current research, please click here.
Date: July 25th, 2014 at 12:00pm
SI Glickman MD Endowed Chair, Professor
MRI Division Chief, RII
Assistant Director for Research, RII
University of Texas Health Science Center
San Antonio, TX
For information on Timothy Duong's current research, please click here.
Date: July 22nd, 2014 at 12:00pm
Professor
Department of Biomedical Engineering
Stony Brook University
Our research encompasses the development of new detector materials and concepts, low-noise microelectronic signal processing, high-throughput data acquisition methods, Monte Carlo simulation, and new data processing techniques to optimize the extraction of quantitative information from the PET data. This talk will present an overview of our unique imaging technologies - RatCAP, small-animal PET-MRI, human breast PET-MRI, wrist scanner for input function, and future human brain imagers.
Date: July 16th, 2014 at 2:00pm
PhD Candidate
Siemens MR Erlangen, MSK Team
Date: July 15th, 2014 at 12:00pm
Assistant Professor of Radiology
New York University School of Medicine
Extensive spatiotemporal correlations in dynamic MRI enable the application of compressed sensing techniques to accelerate data acquisition. Low-rank plus sparse (L+S) matrix decomposition or robust principal component analysis (RPCA) can be employed to represent dynamic images as a superposition of a background component (L) and a dynamic component (S). The dynamic component can include, for example, organ motion or contrast-enhancement information. The L+S model increases the compressibility of dynamic images with respect to L- or S-only models and performs automatic background suppression in the S component. This talk will describe how the L+S model can be employed to reconstruct undersampled dynamic MRI data with automatic separation of background and dynamic components. An extension of the L+S approach that incorporates a motion model to improve the performance in the presence of organ motion will also be discussed. Reconstruction of highly-accelerated dynamic MRI data corresponding to cardiac perfusion, cardiac cine, time-resolved peripheral angiography, and abdominal perfusion using Cartesian and golden-angle radial sampling will be presented to show feasibility and general applicability of the L+S method.
Ricardo Otazo is an Assistant Professor of Radiology at New York University School of Medicine. He received his B.Sc. in Electronics Engineering from Universidad Catolica de Asuncion, Paraguay, in 2001, and his M.Sc. and Ph.D. in Electrical Engineering from the University of New Mexico in 2005 and 2007, respectively. His research interests include the development of rapid MRI and low-dose CT techniques using compressed sensing, image reconstruction algorithms, application of MRI techniques to clinical studies, and signal processing methods in general.
Date: June 25th, 2014 at 2:00pm
Assistant Professor Radiology
Mount Sinai Hospital
Diffusion-weighted (DW) MRI is mostly done with single-shot EPI because multi-shot DW MRI is sensitive to motion-induced phase. Solving the multi-shot problem would open up DW MRI to other pulse sequences, allowing gains in resolution and geometric fidelity. In this talk we’ll look at some solutions to this problem in the context of a 3D (massively multi-shot) DW steady-state free precession sequence.
Rafael O’Halloran started his career in MRI at the University of Wisconsin in Madison where he worked in Sean Fain’s group on hyper-polarized Helium-3 MRI of the lung, fast radial imaging, and diffusion. After graduation, he moved to sunny Stanford, California to work with Roland Bammer on DW MRI of the brain with steady state free precession sequences. In January of this year Rafael joined Mount Sinai as Assistant Professor of Radiology and continues to work on diffusion and other interesting contrasts in the brain. He lives with his wife and 22-month-old son on the Upper East Side and is slowly adjusting to life in New York.
Date: June 24th, 2014 at 12:00pm
Associate Professor
Johns Hopkins University
Diffusion MRI utilizes water molecule diffusion to probe brain microstructures and is an important tool to visualize a wide spectrum of pathologies. In recent years, a special diffusion MRI technique, the oscillating gradient diffusion MRI, has shown promise in providing additional information on tissue microstructures. Our research focuses on using oscillating gradient diffusion MRI to bring novel imaging contrasts to visualize structures and pathology in the brain. We have demonstrated that the technique can reveal densely packed neuronal layers in the mouse hippocampus and cerebellum. In a mouse model of neonatal hypoxia ischemia, our results suggest that the technique can detect swelling of glial cells and their processes at 24 hours after insult.
Date: June 20th, 2014 at 12:00pm
Assistant Professor
Department of Computational Science and Engineering
Georgia Institute of Technology
Low-rank plus Sparse matrix decompositions were recently proposed (by Otazo et al.) as a means of separating the background and dynamic components of undersampled dynamic MRI. For typical model sizes, a sequential plane-by-plane 4D reconstruction using an Alternating Direction Method of Multipliers (ADMM) requires a few hours of computation, most of which is spent within 2D non-uniform Fourier transforms (NUFTs) and the proximal map for the nuclear norm, so-called singular-value soft-thresholding (SVT). Due to the structure of the acquisition operator, within each plane, the NUFTs are embarrassingly parallel over both the channels and timesteps, whereas the SVT primarily consists of a QR decomposition of a tall-skinny matrix and is best decomposed within the image domain. It is shown that, with a careful redistribution of the data at each iteration, both the NUFTs and SVTs can be effectively parallelized on thousands of cores and reconstruction times have been observed to reduce from several hours to roughly one minute. Furthermore, the preliminary implementation is made available as part of an open source package, currently named Real Time Low-Rank Plus Sparse MRI (RT-LPS-MRI).
Jack Poulson is an Assistant Professor in the Department of Computational Science and Engineering at the Georgia Institute of Technology. Jack completed his PhD in Computational and Applied Mathematics at UT Austin at the end of 2012 and spent a brief postdoc in Stanford's Department of Mathematics before moving to Georgia Tech in November of 2013.
Date: June 11th, 2014 at 12:00pm
Center for Magnetic Resonance Research
Department of Radiology
University of Minnesota
NMR offers a plethora of tools for investigating tissue properties in vivo. The present presentation aims to describe novel MRI and MRS approaches that have been recently developed in our laboratory, based on the implementation of frequency swept (FS) pulses operating in adiabatic and non-adiabatic regimes. The tissue contrasts generated by such techniques will be explained within the context of rotating frame relaxation mechanisms, magnetization transfer effects, relaxation along a fictitious field (RAFF) in the rotating frames of rank n ≥2 (RAFFn), and MRI with RAFFn preparations using no echo time SWIFT readout. Frequency swept pulses offer unique capabilities to investigate biological systems for both in vivo and high-resolution NMR. Applications to glioma gene therapy, Parkinson's disease, multiple sclerosis, and quantification of protein dynamics will be presented.
Date: June 10th, 2014 at 1:00pm
Assistant Professor
Department of Radiology
University of Minnesota
Proton magnetic resonance spectroscopy (1H MRS) allows the non-invasive measurement of metabolite concentrations, and is a powerful tool to investigate brain biochemistry and metabolism in health and disease. Similar to MRI, 1H MRS benefits from the gain in signal-to-noise ratio which originates in the increased polarization at higher magnetic fields. High magnetic fields also increase chemical shift dispersion, thus emphasizing the characteristic spectral patterns of metabolites and decreasing spectral overlaps. Greater spectral dispersion additionally improves water suppression and spectral editing. The improved sensitivity achieved at high magnetic fields ultimately results in gains in spatial resolution, temporal resolution, and/or reliability of quantification of an increased number of metabolites. Magnetic fields higher than 4 T are widely employed in 1H MRS studies of animal models. Recent progress in magnet technology, gradient system performance, RF coil and pulse sequence design has enabled localized in vivo 1H MRS in humans at ultra-high magnetic fields up to 7 T. Exciting applications of 1H MRS in humans involve the functional studies of the metabolic events occurring during various stimuli. Our group (Mangia et al, 2007a; 2007b) and others (Lin et al, 2012; Schaller et al, 2013) have measured the concentrations of multiple metabolites with unprecedented sensitivity and temporal resolution at 7 T in the human primary visual cortex during paradigms of visual stimulation. These studies provided critical insights into the metabolic events of increased neuronal activity, and shed light on the neurometabolic coupling of astrocytes and neurons.
Date: May 6th, 2014 at 12:00pm
Post-doctoral fellow
New York University School Of Medicine
Langone Medical Center
Diffusion Spectrum Imaging (DSI) is a powerful means for robustly and non-invasively imaging long-range neuronal architecture in the human brain. This robustness is rooted in DSI’s model-independent determination of the Orientation Distribution Function (ODF) through the sampling of the ODF’s Fourier transform in q-space. The large number of q-space samples needed for accurate measurements of the ODF leads to long acquisition times, hindering practical implementation. These long acquisition times can be partially mitigated by multi-slice or multiband techniques where several slices are encoded at the same time. A second hindrance is that practically feasible b-values (e.g., 4000 s/mm2) limit the achievable angular resolution as the angular resolution is proportional to the inverse of the largest distance sampled in q-space when sampling q-space on a Cartesian grid.
In this talk, we will show that these limitations to the practical implementation of DSI can be overcome by radially sampling q-space (RDSI) using a multi-echo stimulated echo diffusion sequence. When sampling q-space along radial lines, each radial line in q-space is directly connected by the Fourier slice theorem to the value of the radial ODF at the same angular location. This has the advantage that the angular resolution depends on the number of radial lines sampled rather than on the maximum b-value. Hence, radial q-space sampling for DSI results in an improved angular resolution at lower b-values compared to Cartesian q-space sampling for a similar number of samples. In addition, the radial sampling lends itself to using a multiple echo stimulated echo diffusion sequence, accelerating the acquisition almost fourfold. The higher diffusion times of the stimulated echoes are also expected to lead to increased anisotropy and better fiber tracking. The findings presented in this talk suggest that radial acquisition of q-space can be favorable for the practical implementation of DSI.
Date: April 29th, 2014 at 12:00pm
Postdoctoral Researcher
NYU Langone Medical Center
Integrated MR-PET systems like the Siemens Biograph mMR allow simultaneous acquisition of PET and MR data. However, image reconstruction is performed separately and results are only combined at the visualization stage. PET images are reconstructed using a variant of Expectation Maximization while MR data are reconstructed with an inverse Fourier transform or iterative algorithms for parallel imaging or compressed sensing. We propose an integrated joint reconstruction framework based on multi-sensor compressed sensing. This approach uses MR and PET data simultaneously during image reconstruction and exploits anatomical correlations between the two modalities. Results will be shown for numerical simulations and in-vivo imaging that demonstrate improvements in image quality of both MR and PET images. We expect that joint reconstruction can provide additional enhancements to the information content of multimodality studies in the future.
Date: April 25th, 2014 at 3:00pm
Staunton Professor of Psychiatry and Pediatrics
University of Pittsburgh
The adolescent period incurs vulnerabilities that undermine survival (risk-taking behaviors) and importantly increase the risk for the emergence of psychopathology. These vulnerabilities have been associated with a protracted maturation of prefrontal executive and striatal motivational systems. The contribution of each of these systems and importantly systems-level processing to cognitive development are not well understood. I will present a set of fMRI and DTI studies that identify developmental changes in functional specificity including prefrontal systems underlying inhibitory control and striatal neurophysiology and function in reward processing. In addition, studies characterizing changes in functional and structural connectivity through adolescence will be discussed. Together, these findings indicate that adolescents have access to executive systems supporting decision-making but in the context of a reactive motivational system underlied by an established though specializing network connectivity.
Date: April 22nd, 2014 at 12:00pm
Associate Prof. Neurobiology
Post-Doctoral Fellow at Research Laboratory of Electronics, MIT
Martinos Center for Biomedical Imaging, MGH
Magnetic Resonance Imaging (MRI) is a safe imaging technology that provides various clinical benefits. Basically, MRI is performed by exciting magnetic spins with radiofrequency (RF) pulses and receiving the response generated by these spins as they relax into their original state. This response is spatially encoded by using the gradient fields and converted to an actual image. Although it is not desirable, the body is exposed to an electric field during the RF excitation of the spins. The electric field distribution may cause heat dissipation in the conductive medium of body tissues. Safety problems related to such local heating arise when patients with medical implants are to be imaged using MRI. Currently, there are more than 1.5 million patients in the US who have active implants (e.g., pacemakers and deep brain stimulators (DBS)) in their bodies. 50 to 75 percent of these patients will need an MRI scan during the lifetime of their devices. Every 5 minutes, a patient is denied an MRI scan because of the safety issues related to an active implanted medical device. A solution towards improving the safety of patients with implants under MRI is crucial.
Date: April 9th, 2014 at 12:00pm
Associate Prof. Neurobiology
Department of Psychiatry, Radiology and Biomedical Engineering
Columbia University
Head of MRI Physics and Engineering
MRI Research Center
New York State Psychiatric Institute
Magnetic resonance (MR) imaging has recently shown unique capabilities in characterization of psychiatric disorders. MR technologies such as voxel based morphometry (VBM), functional MRI (fMRI), magnetic resonance spectroscopy (MRS), and diffusion imaging (DTI) have shown to be capable of visualizing structural and functional manifestation of neural abnormalities and potential for characterizing their expression. MRI provides tools for in vivo examination of neuroanatomy with potential to differentiate among psychiatric and healthy subject groups. Finding the neural substrates of some psychiatric disorders is now within the reach of structural MRI. In addition, structural MRI is more potent when combined with functional and MRS studies. For example, two metabolites, GABA and glutamate have been found to be most prevalent in schizophrenia. Contrary to the early use of MRS, today’s scanners are capable of resolving glutamate-glutamine levels which sheds light on glutametergic biosynthetic pathway in schizophrenia. The great potential of fMRI lies in its ability to detect the BOLD signal in specific brain regions to identify differences of activity between brains of clinical, subclinical and healthy subjects. Arterial spin labeling has shown promise in revealing subtle brain perfusion changes occurring in psychiatric illnesses. DTI has visualized abnormalities in structural connectivity of the brain regions which in their comparison with functional connectivity maps offer a great tool for assessment of the etiology of psychiatric disorders. This talk will offer a brief discussion about MRI applications and their associated perils and payoffs in psychiatry research. In this context, the challenges in the development of the biomarkers for such use of MRI will be discussed. Potentials of MRI in providing new insight into the etiology and pathophysiology of psychiatric disorders will also be discussed.
Date: March 27th, 2014 at 9:00am
Senior Staff Scientist
CT Collaborations R&D
Siemens Healthcare
There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce patient dose; it provides the flexibility to reconstruct images from arbitrary x-ray system geometries and allows one to include detailed models of photon transport and detection physics to accurately correct for a wide variety of image degrading effects. This paper reviews discretization issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. The widespread implementation of IR with a highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modeling.
Date: March 26th, 2014 at 12:00pm
Postdoctoral Associate
Magnetic Resonance Research Center
Department of Diagnostic Radiology
Yale University School of Medicine
Imaging with several detectors concurrently, known as parallel imaging, is a method to accelerate scans. However, parallel imaging encounters diminishing returns when increasing the number of detectors. Nonlinear gradient encoding allows encoding fields to complement the receiver coil detection to reconstruct equivalent images from less data. Nonlinear gradient encoding expands the encoding functions available to efficiently encode MR images. From the literature, nonlinear encoding has been shown to increase resolution in regions of the image, reduce peripheral nerve stimulation, and localize the field of view during radiofrequency excitation. Nonlinear gradient encoding and its optimization for faster images with equivalent image quality are examined. O-space imaging using the Z2 field has previously reported dispersed artifacts during accelerated scans. The inherent incoherence (distributed artifacts) of O-space imaging is explored and optimized within a compressed sensing framework. Null Space Imaging is a generalization of O-space imaging and uses an algebraic method of determining encoding fields from coil receiver profiles. Gradient hardware to perform nonlinear encoding is featured, including a 12 cm ID Z2 gradient wrist imaging insert, a 38 cm ID Z2 neuroimaging insert, and a 10 channel 20 cm ID gradient insert. The resulting body of work suggests nonlinear gradient imaging is a flexible and advantageous improvement on traditional parallel MR imaging.
Date: March 25th, 2014 at 12:00pm
Assistant Professor of Radiology
New York University School of Medicine
At high and ultra-high magnetic field strengths, understanding interactions between tissues and the electromagnetic fields generated by radiofrequency coils becomes crucial for safe and effective coil design as well as for insight into limits of performance. In this work, we present a rigorous electrodynamic modeling framework, using dyadic Green’s functions, to derive the electromagnetic field in homogeneous spherical and cylindrical samples resulting from arbitrary surface currents. We show how to calculate ideal current patterns that result in the highest possible signal-to-noise ratio (ultimate intrinsic signal-to-noise ratio) compatible with electrodynamic principles. We identify familiar coil designs within optimal current patterns at low to moderate field strength, thereby establishing and explaining graphically the near-optimality of traditional surface and volume quadrature designs. We also document the emergence of less familiar patterns, e.g., involving substantial electric- as well as magnetic-dipole contributions, at high field strength. Performance comparisons with particular coil array configurations demonstrate that optimal performance may be approached with finite arrays if ideal current patterns are used as a guide for coil design.
Date: March 18th, 2014 at 12:00pm
Associate Professor
PROVIDI Lab
Image Sciences Institute
University Medical Center Utrecht
The Netherlands
With its unique way of characterizing tissue organization, diffusion MRI (dMRI) has been used in a wide range of clinical and biomedical applications. In addition to a brief introduction to the basic concepts of dMRI, I will cover practical guidelines on quality and processing of dMRI data for subsequent analysis. Several considerations regarding dMRI limitations and data interpretation will also be presented. For relevant background information, see for instance http://www.ncbi.nlm.nih.gov/pubmed/21469191 and http://www.ncbi.nlm.nih.gov/pubmed/22846632.
Alexander Leemans is a physicist who received his PhD in 2006 at the University of Antwerp, Belgium. From 2007 to 2009, he worked as a postdoctoral researcher at the Cardiff University Brain Research Imaging Center (CUBRIC), Cardiff University, Wales, United Kingdom. In 2009, he joined the Image Sciences Institute (ISI), University Medical Center Utrecht, the Netherlands, where he currently holds a tenured faculty position as Associate Professor. He heads the PROVIDI Lab and is the developer of ExploreDTI, which is a graphical toolbox for investigating diffusion MRI data.
Date: February 28th, 2014 at 12:00pm
Institute for Biomedical Engineering
University and ETH Zurich
Date: February 25th, 2014 at 3:00pm
Professor
Departments of Radiology, Electrical Engineering and Biomedical Engineering
University of Minnesota
UHF MRI requires new RF technology and methods to realize the full high field benefit to biomedical science and clinical diagnostics. New multi-channel transmitters, receivers, and safety monitoring methods for 3T, 7T, and 10.5T are included. New approaches to these UHF challenges are being developed in Minnesota, at NYU, and at other luminary labs around the world. This talk will present some of Minnesota's work, will invite sharing from NYU's experience, and will provide a forum for mutual discussion of approaches taken by other labs. The results of this presentation and following discussions will lead to formulation of future collaborations between NYU and the UMN.
Date: February 25th, 2014 at 12:00pm
Assistant Professor of Radiology
The Children's Hospital of Philadelphia (CHOP) and the University of Pennsylvania, Perelman School of Medicine
A goal of neurosurgery is to preserve both functionally important cortices and the underlying white matter tracts. Diffusion MR tractography is a non-invasive method of visualizing the 3D course of white matter tracts. Traditional DTI fiber tracking is widely used for surgical planning, but fails to accurately represent the microstructure of crossing white matter tracts. The insufficiencies of DTI have motivated the application of high angular resolution diffusion imaging (HARDI) tractography to neurosurgical planning. This talk will describe the development, validation, and clinical utility of diffusion MR tractography for surgical planning, with an emphasis on recent HARDI techniques.
Jeffrey Berman is an Assistant Professor of Radiology at the Children's Hospital of Philadelphia (CHOP) and the University of Pennsylvania, Perelman School of Medicine. He received his PhD from the joint UC Berkeley - UC San Francisco bioengineering graduate program where he developed and validated diffusion MR techniques for surgical planning. During a postdoc at UC San Francisco, he used diffusion MR to study the developing brain of premature and term infants. At CHOP since 2010, his research interests include combining diffusion MR with MEG to study neuropsychiatric disorders such as autism and developing advanced diffusion MR tools for surgical planning.
Date: February 12th, 2014 at 12:00pm
Instructor of Medicine, Harvard Medical School
Senior Research Scientist, Beth Israel Deaconess Medical Center
One of the major challenges of cardiac MRI is its lengthy acquisition, which limits the achievable spatial and temporal resolutions, and volumetric coverage. In this talk, we will discuss novel compressed sensing (CS) based image reconstruction techniques used for accelerating data acquisition in cardiac MRI. We will introduce techniques that utilize patient and anatomy-specific information to improve reconstruction quality with respect to standard CS methods, as well as to state-of-the-art parallel imaging techniques. We will validate these techniques in accelerated high-resolution coronary artery and late gadolinium enhancement imaging. We will also extend these acceleration techniques to accelerated perfusion cardiac MRI with free-breathing applicability. We will conclude with a brief overview of ongoing research, as well as future research directions.
Date: February 11th, 2014 at 12:00pm
Assistant Professor
Department of Radiology
New York University School of Medicine
Renal cancers are being increasingly diagnosed incidentally. Some of these tumors are aggressive, whereas others have a relatively indolent course. Current morphologic imaging is limited in assessment of tumor aggressiveness. Promising MR techniques such as intravoxel incoherent diffusion weighted imaging (IVIM) and dynamic contrast-enhanced (DCE) imaging will be discussed. Unsolved problems and clinical needs will be highlighted. There is also a need to develop and validate better techniques to assess renal function. MR techniques such as DCE, DTI, and BOLD have shown considerable early promise.
Dr. Chandarana is an abdominal radiologist and clinical scientist in the Department of Radiology, New York University School of Medicine, with interest in advanced oncologic and functional imaging.
Date: February 4th, 2014 at 12:00pm
Postdoctoral Fellow
Department of Radiology
New York University School of Medicine
The sensitivity of time-dependent diffusion to the overall structure of its environment makes it an appealing tool in the study of white matter fibers. Previous studies have mainly focused on increasing the q value as much as possible under a clinical system. In contrast, we vary the diffusion time, t, which allows us to probe the structure by increasing the diffusion length. We observe via DTI measurements on a fiber phantom that the long-time diffusion exhibits a unique (log t)/t dependence transverse to fibers as a result of disordered packing. This has implications in a variety of diffusion experiments such as oscillating gradients and axon diameter estimation. This leads to the question of whether or not time-dependent diffusion is even observable on a clinical scanner. So far, the literature is inconclusive. We scan five healthy volunteers using a DTI protocol with diffusion times ranging from 26 to 400 ms and find that we indeed do see time dependence parallel and perpendicular to the axons. The effect is strongest along the axonal direction possibly indicative of heterogeneities within axonal fibers.
Date: January 28th, 2014 at 12:00pm
Director of the Institute of Neurosciences and Medicine
Forschungszentrum Julich, Germany
Date: January 21st, 2014 at 12:00pm
Postdoctoral Fellow
Department of Radiology
New York University School of Medicine
In clinical neuroimaging, perfusion MRI is of spectacular importance to study cerebrovascular diseases and cancer. However, at the moment, there is no perfusion MRI sequence that allows for a complete, non-invasive and precise quantification of microvascular flow dynamics. This work focuses on the use of the recently introduced Flow Enhanced Signal Intensity method (FENSI) to characterize and quantify vasculature at capillary level, at high magnetic field (7T). For that purpose, the possible quantification of blood flux with FENSI is explored in vivo. The combination of flux quantification and flow-enhanced signal (compared to Arterial Spin Labeling) can make of FENSI an ideal method to characterize in a complete non-invasive way the brain microvasculature. After removal of magnetization transfer (MT) effects, the blood flow dynamics are studied with FENSI in a very aggressive and propagative rat brain tumor model: the 9L gliosarcoma. The objective is to assess whether FENSI is suitable for a longitudinal non-invasive characterization of microvascular changes associated with tumor growth. The results obtained with FENSI are compared with literature on 9L perfusion and immuno-histochemistry. Functional MRI might also benefit from the development of flow enhanced MRI. With the implementation of a new MT-free FENSI technique, the possibility to map the brain cerebral functioning based on a quantitative physiological parameter (CBFlux) more directly related to neuronal activity than the usual BOLD signal is within reach. Preliminary results on rats and human brain are presented.
Date: January 14th, 2014 at 12:00pm
Postdoctoral Fellow
Department of Radiology
Center for Biomedical Imaging
New York University Langone Medical Center
In recent years, there is increasing recognition of cerebral microbleeds (MCBs) in patients with cerebrovascular diseases and dementia with MRI, in particular at high-field-strength. The detection of CMBs or lesions with small iron components (i.e. amyloid plaques) depends on several MRI characteristics including field strength, pulse sequence, imaging parameters, spatial resolution, iron concentration, and image post-processing. We hypothesized that using optimal imaging sequence/parameters, there is a significantly enhanced blooming effect (i.e. larger area than the actual object size) at high field MR, which has potential to detect much smaller iron-containing lesions or structures. In this study, we used 3D gradient-echo imaging to quantify the susceptibility blooming factor (i.e. detected size/real size of object) based on a tube phantom with different iron concentrations and post-mortem brain slices. Ultra-high-field MR (e.g. 7T) provides superb susceptibility contrast (i.e. marked blooming effects) to enhance the capability of the detection of small lesions that contain iron components. We have characterized and demonstrated the actual degree, enhanced visibility, and imaging optimization of the blooming effects based on the results of phantom, simulation, and clinical images on 7T as compared to standard 3T and 1.5T MR. We investigate the use of available iron contrast agents to find the optimal parameters in a clinical perspective at high field (7T). Our results suggest that a 3D gradient echo with optimal TE and voxel size helps to detect even small quantities of iron. We establish each blooming factor (measured size/actual size) for specific TE, iron concentration, or spatial resolution on 7T as compared to 3T and 1.5T. The blooming factor may provide a tool to approximate the actual size of structures even smaller than a voxel.
Date: December 19th, 2013 at 2:00pm
Associate Professor of Radiology
Musculoskeletal Imaging and Intervention Division Department of Radiology Massachusetts General Hospital
Harvard Medical School
Dr. Bredella's research is at the interface of radiology and endocrinology. In this talk, she will describe her work investigating the effects of different kinds of fat depots on bone density, structure, strength, and marrow fat in obesity and anorexia nervosa. She is also investigating the role of growth hormone in improving bone health and decreasing cardiovascular risk in obesity. Dr. Bredella attended medical school at the University of Hamburg in Germany. She subsequently worked for 2 years at the Osteopororsis and Arthritis Research Group at UCSF under Harry Genant. She completed her residency at UCSF followed by a musculolskeletal radiology fellowship at MGH, where she has been on staff since 2005. She is currently an Associate Professor of Radiology at Harvard Medical School. She was previously awarded an NIH K23 grant and most recently received an R01 grant focusing on skeletal dysregulation in obesity. She is also a co-investigator on an R24 grant examining the role of marrow fat.
Date: December 13th, 2013 at 11:00am
Director of Sackler Institute for Developmental Psychobiology
Professor of Developmental Psychobiology
Weill Medical College of Cornell University
BJ Casey is the Sackler Professor and Director of the Sackler Institute at Weill Medical College of Cornell University. She is a pioneer in novel uses of neuroimaging methodologies to examine behavioral and brain development. Her program of research focuses on attention and affect regulation, particularly their development, disruption, and neurobiological basis. She has been examining the normal development of brain circuitry involved in attention and behavioral regulation and how disruptions in these brain systems (prefrontal cortex, basal ganglia, and cerebellum) can give rise to a number of developmental disorders. Using a mechanistic approach, she has dissociated attentional deficits observed across the disorders of Attention Deficit Hyperactivity Disorder, Obsessive Compulsive Disorder, Tourette Syndrome, and Childhood Onset Schizophrenia.
Date: December 10th, 2013 at 12:00pm
Research Scientist
Department of Radiology
Center for Biomedical Imaging
NYU School of Medicine
The Siemens Biograph mMR installed in the CBI (first floor of 660 First Ave) allows simultaneous acquisition of MR and PET data. Although spatially and temporally aligned raw data is available, both modalities are often treated separately and corresponding images are only fused after independent reconstruction. This talk gives an overview of current research projects in the CBI where MR information is used to improve the PET image reconstruction. These projects include motion detection, motion correction, and finally joint reconstruction of PET and MR data.
2012 - 2013: Development Engineer at GEA
2010 - 2012: PostDoc at European Institute for Molecular Imaging (Muenster, Germany)
2006 - 2010: PhD in Mathematics at WWU (Muenster, Germany)
2001 - 2006: Mathematics at WWU (Muenster, Germany)
Date: December 3rd, 2013 at 12:00pm
Postdoctoral Fellow
Department of Radiology
NYU School of Medicine
Recent progress in MRI has opened the way for micron-scale resolution, and thus for imaging biological cells. The goal of my thesis work was to perform magnetic resonance microscopy (MRM) on the nervous system of Aplysia californica, a model particularly suited due to its simplicity and to its very large neuronal cell bodies, in the aim of studying cellular-scale processes with various MR contrasts. Experiments were performed on a 17.2 T horizontal magnet, at resolutions down to 25 µm isotropic. Initial work consisted in conceiving and building radiofrequency microcoils adapted to the size of single neurons and ganglia. The first major part of the project consisted in using the manganese ion (Mn2+) as neural tract tracer in the nervous system of Aplysia. We performed the mapping of axonal projections from motor neurons into the peripheral nerves of the buccal ganglia. We also confirmed the existence of active Mn2+ transport inside the neural network upon activation with the neurotransmitter dopamine. In the second major part of the project, we studied the changes in water ADC at different scales in the nervous system, triggered by cellular challenges. A 3D Diffusion-Prepared FISP sequence was first implemented, which met criteria for high resolution in a short acquisition time, with minimal artifacts. Using this sequence, ADC measurements were performed on single isolated neuronal bodies and on ganglia tissue, before and after two types of challenge (hypotonic shock and ouabain). Both types of stress produced an ADC increase inside the cell and an ADC decrease at tissue level. The results favor the hypothesis that the increase in membrane surface area associated with cell swelling is responsible for the decrease of water ADC in tissue, typically measured in ischemia or other conditions associated with cell swelling.
Date: November 26th, 2013 at 12:00pm
Professor of Radiology
Miller School of Medicine
University of Miami
MR spectroscopic measurements of human brain are commonly limited to small regions to minimize difficulties associated with magnetic field inhomogeneities and lipid contamination; however, several clinical applications could greatly benefit from obtaining MRS measurements over larger brain volumes, including for example, measurement of diffuse tissue injury with traumatic brain injury, characterization of tumor volumes for therapy planning, and localization of neocortical epilepsy. This presentation will review some of the experimental approaches that can be used to extend the measurement volume for MR spectroscopic imaging, and show examples of clinical applications of these methods.
Date: November 19th, 2013 at 12:00pm
Department of Neuroradiology and Nuclear Medicine
TUM-Neuroimaging Center
Klinikum Rechts der Isar der Technischen Universität München (TUM), Germany
Recent functional magnetic resonance imaging (fMRI) revealed organized activity in the brain at rest which gained enormous relevance for systems and clinical neuroscience. Particularly, this organized activity is defined by synchronous, low frequency (<0.1Hz) fluctuations of the blood-oxygenation-level-dependent (BOLD) fMRI signal between remote brain areas, termed resting-state functional connectivity (rs-FC). However, the neurophysiological and metabolic underpinnings of rs-FC are still incompletely understood. In this talk I will summarize recent findings of rs-fMRI and present first data of simultaneous PET/MR imaging in humans indicating a neuronal basis of resting state FC.
Date: November 5th, 2013 at 12:00pm
Assistant Professor
Department of Radiology
Center for Biomedical Imaging
New York University Langone Medical Center
CBI's RF lab (second floor of 660 First Ave) houses a 3D printer that provides the capability to build a wide range of MRI compatible fixtures. This talk is aimed at educating potential users on the printer's general capabilities for rapid prototyping. Specifications on CAD software, build-size, resolution, and print speed will be reviewed in the context of objects designed at CBI during the past year. While many of the examples are hardware-related fixtures such as anatomically-correct and aesthetically-pleasing RF coil formers, it is anticipated that the lecture will spark interest in a more expansive range of applications.
Date: October 30, 2013 at 12:00pm
Institute of Biomedical Engineering and Informatics
Ilmenau University of Technology
Ilmenau, Germany
For compensating the signal loss in GRE-based sequences induced by through-plane susceptibility, two state-of-the-art techniques using the parallel transmit technology (pTX) were analyzed. Both approaches, the tailored 3-dimensional RF pulses (3DTRF) and time-shifted spokes excitation, were implemented on the 3T Skyra system with two integrated whole-body transmit channels. The methods were extended and evaluated with human in-vivo experiments.
Date: September 24, 2013 at 12:00pm
Vision Lab, University of Antwerp
Antwerp, Belgium
Diffusion magnetic resonance imaging (dMRI) is currently the method of choice for the in vivo and non-invasive quantification of water self-diffusion in biological tissue. Several diffusion models have been proposed to obtain quantitative diffusion parameters. Those parameters might provide novel information on the structural and organizational features of biological tissue, the brain white matter in particular. However, an accurate and precise estimation of those diffusion parameters remains challenging because of the non-Gaussian MR data distributions. Indeed, widely used estimator – e.g. the class of least squares estimators – will show systematic errors in the estimation of diffusion measures because the actual data statistics are not taken into account. The squashing of the ADC peanut or the overestimation of the kurtosis metrics are typical examples of such, so called, noise artifacts. During the seminar, an overview of the commonly used parameter estimators will be given. Their strengths and limitations will be discussed. In addition, a comprehensive framework for accurate diffusion MRI parameter estimation will be introduced.
Date: September 17, 2013 at 12:00pm
New York University School of Medicine
New York, New York
The development of biomarkers for the preclinical detection of Alzheimer’s disease (AD) is a vital step in developing prevention therapies. For many years, we and others have been using biological markers of AD pathology and its effects on brain structure and function to characterize early changes in presymptomatic individuals at risk for AD. Such markers include in vivo brain Magnetic Resonance Imaging (MRI); Positron Emission Tomography (PET) imaging using 2-[18F]fluoro-2-Deoxy-D-glucose (FDG) and N-methyl[ 11C]2-(4'-methylaminophenyl)-6-hydroxy-benzothiazole (PiB) as the tracers to measure glucose metabolism and fibrillar amyloid-beta (Aß) deposition, respectively; cerebrospinal fluid levels of Aß1-40 and 1-42, tau pathology (total tau and hyperphosphorylated tau231) and inflammation (F2-isoprostanes); and recently plasma measures of oxidative stress (activity of mitochondria cytochrome oxidase, electron transport chain complex IV, COX).
This lecture will give an overview of biomarker findings in individuals at risk for AD, with the main focus on presymptomatic individuals carrying genetic mutations responsible for early-onset familial AD and cognitively normal (NL) people with a first degree family history of LOAD. Overall, these studies have shown that it is possible to identify and track biomarker changes prior to cognitive impairments arise and along with AD progression. All told there is considerable promise for an early and specific diagnosis of AD by assessing biomarkers in NL individuals at risk for AD.
Date: August 20, 2013 at 12:00pm
Professor of Physics in Radiology
Weill Medical College of Cornell University
New York, New York, USA
The most widely investigated neurochemical hypotheses of major psychiatric disorders now posit neurodevelopmental deficits that involve, among others, dysregulations of the inhibitory and excitatory amino neurotransmitter systems of gamma-Aminobutyric acid (GABA) and glutamate (Glu), respectively. Glutathione (GSH) is a major intracellular antioxidant and redox regulator, whose dysregulations and in vivo deficits have been implicated in various neurological, neuropsychiatric and neurodegenerative disorders. Currently, proton magnetic resonance spectroscopy (1H MRS) is the only noninvasive neuroimaging technology that offers the possibility to investigate abnormalities in GABA, Glu and GSH in the living human brain. In this presentation, our decade-long experience in developing and optimizing the relevant MRS technology will first be described, and then the full power and growing importance of the technology in biomedical and neuroscience research will be illustrated with selected clinical applications in neuropsychiatry and neurology.
Date: July 30, 2013 at 12:00pm
Nathan Kline Institute for Psychiatric Research,
Child Mind Institute, and
New York University Child Study Center
Resting-state functional magnetic resonance imaging (R-fMRI) has emerged as a mainstream imaging modality with myriad applications in basic, translational and clinical neuroscience. Beyond impressive demonstrations of accuracy, reliability and reproducibility for measures of intrinsic brain function, this approach has gained popularity due to its sensitivity to developmental, aging and pathological processes, ease of data collection in otherwise challenging populations, and amenability to aggregation across studies and sites. In this talk, I would like to introduce the principles, computational algorithms and methodological issues of R-fMRI as well as its clinical application to brain disorders (e.g., Alzheimer's disease). Finally, I would like to demonstrate the data processing of R-fMRI with our convenient pipeline toolbox DPARSF.
Date: July 29, 2013 at 12:00pm
Professor
Institute for Mathematics and Scientific Computing
University of Graz
We discuss the recently introduced total generalized variation (TGV) which is a well-suited regularizer for variational imaging problems. In addition to the well-known total variation (TV), it does not only model free discontinuities but is also aware of higher-order smoothness. It can be interpreted as a regularizer which adaptively selects the appropriate smoothness level.
After studying basic properties of the TGV functional, we show how abstract methods for finding convex-concave saddle point problems can be applied to solve variational imaging problems with TGV-regularization. Several applications are presented, ranging from basic imaging problems like denoising and deconvolution to applications in MRI, CT and compressed sensing.
Finally, we show the potential of general measure-based regularization beyond TV and TGV. In particular, convex regularization functionals are discussed which are able to count vertices and edges. Furthermore, their application to the reconstruction of elongated structures is presented.
Date: July 17, 2013 at 12:00pm
Associate Professor
Department of Biomedical Engineering
Technion-Israel Institute of Technology
PET is a powerful modality in medical imaging. However, its spatial resolution is very poor compared to other major modalities (CT, MRI and Ultrasound). The challenge is to improve PET image quality without inserting any physical changes in the scanner hardware. In this lecture two approaches will be introduced. The first approach is to implement super-resolution strategy. With super-resolution several low resolution images are acquired, where each image is shifted by a sub pixel distance relative to the other. An algorithm is then implemented to combine the information and produce a high resolution image. The second approach is implemented on data acquired by a hybrid PET-CT scanner. The images obtained from the CT are fused with the images obtained from the PET using an algorithm called "Hybrid Computerized Tomography (HCT)". The obtained images depict sharp border PET distribution.
Date: June 19, 2013 at 9:00am
Assistant Professor
Biomedical Engineering
Case Western Reserve University
Assistant Professor
Director of MRI
Departments of Radiology, Urology
Case Western Reserve University
Date: June 13, 2013 at 9:00am
Associate Professor
Department of Radiology
University of Minnesota
Date: May 28, 2013 at 12:00pm
NYU Langone Medical Center
Magnetic resonance spectroscopy is used routinely to measure metabolite concentrations in the human brain. Due to fast relaxation times and complex J-coupling patterns, many of the most important metabolites observable with spectroscopy - such as GABA and Glutamine/Glutamate - are difficult to discern using standard spectroscopic techniques. In this talk, I will argue why short echo times (<10 ms) offer significant benefits when trying to image such metabolites, why in-vivo spectroscopy has only fairly recently begun exploring these possibilities, and present our own approach for doing so using radiofrequency Hadamard pulses.
I will also briefly discuss two other projects which may be of interest to other researchers at the CBI: our approach to dealing with B0 field drifts, as well as our approach to analyzing global white/grey matter metabolite concentrations.
Assaf Tal obtained his BSc in physics from the Hebrew University in Israel, and his PhD from the Weizmann Institute of Science in Israel with Prof. Lucio Frydman in the field of liquid state NMR, where he has done work on single-scan methods in 2D NMR as well as fast imaging methods based on quadratic spin phase. His current post-doctoral research in the lab of Oded Gonen focuses on developing new sequences and processing methodologies for in-vivo human brain spectroscopic imaging.
Date: May 15, 2013 at 12:00pm
Research Associate
Department of Radiology, University of Pennsylvania
Non-invasive differentiation of brain abscesses such as pyogenic and tuberculous, anaerobic and aerobic or sterile is essential for facilitating prompt and appropriate treatment of patients. MR spectroscopy and magnetization transfer MR imaging may be used to characterize intracranial cystic lesions with similar features on conventional MR imaging. Precise MR imaging correlation of different stages of neurocysticercosis with histopathology is essential for better understanding of the disease that is usually hampered by complexities in performing such studies on humans. Therefore, detailed correlative MR imaging and histopathological studies on pigs infected with neurocysticercosis are warranted.
Given the heterogeneous nature of neoplastic lesions and inherently different physiological information provided by different MR pulse sequences, multi-parametric data analysis may be a better approach in differential diagnosis, predicting prognosis, monitoring treatment response in brain tumors and head and neck cancers with greater accuracy. Combined use of MR spectroscopy and perfusion weighted imaging may be used to distinguish histological grades, histological subtypes and genetic profiles of the gliomas.
Date: May 10, 2013 at 12:00pm
Lecturer on Radiology
Department of Radiology, Brigham and Women’s Hospital, Boston, MA
Conventional magnetic resonance (MR) imaging scans suffer from limited resolution that prohibits the visualization of individual cells thus providing information at coarse length scales. To obtain information at smaller length scales, the MR signal can be sensitized to self-diffusion of water molecules whose motional history is influenced by the local microstructure. I will present several new developments in the field of diffusion-weighted MRI. Emphasis will be given to the multiple pulsed field gradient techniques, which could be used to characterize the local microstructural features of the medium without the need to employ strong magnetic field gradients. In the second part of the talk, I will describe the recently introduced mean apparent propagator (MAP) MRI technique, which is a comprehensive computational framework that could be employed to address a number of challenges encountered in the analysis of diffusion-weighted MRI data.
Evren Özarslan is a research associate at Brigham and Women's Hospital and holds a concurrent academic appointment at Harvard Medical School (HMS). Before joining HMS, Dr. Özarslan performed research at the Section on Tissue Biophysics and Biomimetics (STBB), NICHD, National Institutes of Health (NIH) first as a postdoctoral fellow, then as a scientist with the Center for Neuroscience and Regenerative Medicine (CNRM) and the Henry M. Jackson Foundation. He graduated with a Bachelor of Science in Physics from the University of Illinois at Urbana-Champaign, and obtained his M.S. degree in Biomedical Engineering and Ph.D. in Physics, both from the University of Florida. His current research is on modeling diffusion in biological tissue and other porous media with the specific aim of characterizing the microstructure of the specimen using noninvasive magnetic resonance techniques.
Date: April 30, 2013 at 12:00pm
Department of Radiology, University of Lausanne
Switzerland Functional and Metabolic Imaging Laboratory, EPFL, Switzerland
Date: April 16, 2013 at 12:00pm
Professor of Radiology
NYU Langone Medical Center
In a growing number of studies and applications, strategic selection and placement of passive high-permittivity materials are shown to improve SNR and/or reduce required transmit power in imaging a select region of interest. We will discuss some basic mechanisms by which high-permittivity materials can improve RF efficiency in MRI and review a variety of cases where they have been demonstrated to do so.
Date: April 5, 2013 at 12:00pm
University of Michigan
In this talk, I discuss my research concerning sparse modeling for magnetic resonance imaging. First, I elaborate on three methods for using sparsity to improve upon GRAPPA, an autocalibrating reconstruction method for accelerated parallel imaging. These three methods (1) denoise the reconstructed k-space, (2) regularize the calibration of the GRAPPA kernels, and (3) jointly estimate the full k-space and GRAPPA kernels using prior and likelihood models.
All these methods make use of fixed parameters that control the regularization. While hand-tuning these methods may be possible, we desire an automatic parameter selection method that would work for data-preserving reconstructions. To this end, I introduce Stein's Unbiased Risk Estimate and describe how I extend it to data-preserving regularized parallel imaging reconstructions.
I follow this discussion by outlining my current research exploiting sparsity to prospectively correct for head motion in functional MRI. I demonstrate that this usage of sparsity allows for high-quality time-series correlation analysis in the presence of head motion.
Date: April 2, 2013 at 12:00pm
Professor, Department of Radiology
Johns Hopkins University School of Medicine, Baltimore, MD
One of the most challenging aspects of image analysis is the overwhelming amount of spatial information. For example, typical T1-weighted image with 1mm resolution contains more than 1 million voxels, each of which carry noisy information. Cross-contrast (e.g. T1 and DTI) and cross-modality (e.g. MRI, MRS, fMRI, PET) data integration have been postulated as potentially a powerful approach to delineate anatomical and functional phenotype of patient populations, which would lead to further increase in spatial information with different coordinate frames and, thus, a systematic reduction of the spatial dimension seems an essential and inevitable requirement. This presentation will introduce our current effort to establish a modern MRI atlas system and associated software tools to perform atlas-based image analysis, in which the entire spatial information is reduced to approximately 200 pre-defined structures. For demonstration, integrative analyses of anatomical MRI, DTI, MRS, and rs-fMRI data and clinical applications will be shown. The automated pipeline for the atlas-based analysis is currently being deployed using a cloud-based architecture for dissemination and future direction of the service model will also be discussed.
Date: March 26, 2013 at 12:00pm
Senior Research Scientist/Chief MR Physicist
New York University
Center for Brain Imaging
Functional MRI data quality can be compromised by a series of factors -- especially motion and spikes -- which are hard to assess until you start processing your data, well after the scanning session is over. By that time, your subject is gone and you might find you are left with too little data to be able to include that subject in your analysis. I will present the implementation of a real-time data-quality monitoring tool that reconstructs the images, estimates motion parameters and some other statistics on them, and displays them on the screen as they are being acquired, so that users can repeat those runs with excessive motion or with spikes, and give feedback to the subject on how well he or she is avoiding motion in the scanner.
Date: March 5, 2013
PhD in Neuroradiology at University College London-UCL, London England
Low-grade gliomas in adults are diffusively infiltrating tumours that may undergo malignant transformation into high-grade gliomas. This malignant transformation is highly variable and difficult to predict in an individual patient. The purpose of this study was to investigate the value of conventional and advanced magnetic resonance imaging in patients with histology-proven low-grade gliomas and the potential role of these methods as markers of malignant transformation.
Date: February 26, 2013
Doctoral Candidate
The Sackler Institute of Graduate Biomedical Sciences
New York University School of Medicine, NY
"By the end of 2012, the number of mobile-connected devices will exceed the number of people on Earth, and by 2016 there will be 1.4 mobile devices per capita." - Cisco VNI Mobile 2012.
Radio frequency (RF) emitting wireless devices such as mobile phones are required to undergo standardized safety testing prior to entering the consumer market. Strict regulations are imposed on the amount of RF energy these devices are allowed to emit to prevent excessive deposition of RF energy into the body. In this presentation, a novel safety evaluation test for wireless devices using magnetic resonance (MR) thermometry is proposed.
Date: February 5, 2013
Senior Lecturer and Director
Laboratory of Cellular Biophysics and Imaging
Tel-Aviv University, Israel
Diffusion Weighted NMR (DW-NMR) of tissues characterizes two linked cellular properties: microstructure and viability. DW-NMR in cells is affected by structures that restrict and hinder diffusion. Following brain insults, such as ischemia, water displacement is attenuated and is commonly linked to microstructural changes affecting diffusion. Water displacement is linked not only to microstructure but also to cellular viability and function, as in the case of neuronal activity that is suggested to be correlated with restricted diffusion.
We attempt to quantify the different components of water displacement in cells, in order to obtain an accurate characterization of cells' microstructure and function. In the coming lecture I will first describe our method for quantifying pore size distribution, towards the characterization of cells' sizes. This is done by the use of a double pulsed field gradient experiment, in which gradient pairs are varied by amplitude and direction.
A central hypothesis in our research is that diffusion is not the only component of displacement in cells: we suggest that a significant component of water displacement in neurons is that of actively induced micro-streaming. I will describe our theoretical and experimental work aiming to quantify the relation of function and micro-streaming inside neurons. This is done by using biophysical models and by DW-NMR of isolated and viable neural tissues. I will end by speculating the possible implications of our work on brain function study.
Date: January 29, 2013
Doctoral Candidate
The Sackler Institute of Graduate Biomedical Sciences
New York University School of Medicine
The first part of the talk will cover the dependence of the B1 spatial distribution on the electrical properties of the sample and the magnetic field strength. Unanticipated B1 field patterns may be encountered during simulation and experiments, particularly at high operating frequencies. While a distinctive curling of the B1 field is observed at high field strengths, elaborate checkerboard-like patterns may be obtained for certain dielectric samples. In this work, we use full-wave electrodynamic simulations based on dyadic Green's functions to study the effect of the electrical properties of the sample and main magnetic field strength on the B1 field pattern inside a uniform cylindrical object. We show examples of the curling of the field and interference patterns near resonance, providing a conceptual explanation for each case.
In the second half, manipulation of the B1 field distribution inside a sample by placing dielectric pads at a distance from a surface transmit coil will be discussed. The use of dielectric pads between the radiofrequency (RF) coil and sample has been proposed to "focus" the B1 field into the sample to improve transmit efficiency. In this study, we investigated how dielectric pads placed at a distance from the RF coil affect the B1+ spatial distribution inside the sample. We performed numerical simulations of the B1+ distribution inside a uniform cylinder at 7T for various positions of the dielectric pad with and without a surrounding shield. Manipulating B1 spatial distribution with dielectric pads can be advantageous for various MR applications, including improving RF homogeneity at ultra-high fields.
Date: January 15, 2013
Assistant Professor of Radiology
The Bernard and Irene Schwartz Center for Biomedical Imaging
New York University Langone Medical Center, NY
A major challenge of in vivo MR is to characterize tissue microstructure at the cellular level, orders of magnitude below the imaging resolution. I will show how a diffusion measurement, taken at a range of diffusion times, can distinguish between different classes of microgeometry. Based on the specific values of the dynamical exponent of a velocity autocorrelator measured with diffusion MRI, we identify the relevant tissue microanatomy in muscles and in brain, quantify cell membrane permeability in muscles, and reveal the microstructural changes driving the diffusivity drop in ischemic stroke. Our framework presents a systematic way to identify the most relevant part of structural complexity with diffusion.
Date: January 10, 2012
Doctoral Candidate
The Slacker Institute of Graduate Biomedical Sciences
New York University School of Medicine, NY
Significant progress has been made in our understanding of the pathogenesis of brain tumors partly due to the development of genetically engineered mouse models that recapitulate the human disease. In this regard, in vivo micro-MRI protocols are powerful tools for the non-invasive, three-dimensional (3D) characterization of these preclinical cancer models and are gradually being recognized as an integral part of basic and translational brain tumor research. In our study, we optimized an in vivo high resolution Manganese-Enhanced MRI protocol (MEMRI) for the characterization of tumor progression in a novel mouse model of Medulloblastoma (MB), the most common malignant pediatric brain tumor originating in the cerebellum (Cb).
In this talk, I will present the characteristics of our tumor model and show that our imaging approach successfully allowed the detection of early tumoral lesions and the longitudinal assessment of their progression into advanced-stage tumors. Furthermore, I will discuss recent results which indicate that these tumors display at least two distinct molecular and imaging features. Ultimately, we are interested in correlating these findings with the clinical and imaging characteristics of human MBs and we expect to draw insights that inform the design of studies to test current and novel drug therapies using this unique pre-clinical model.
Date: February 9, 2012
Assistant Professor of Psychiatry and Neurology
Epilepsy Center
New York University Langone Medical Center, NY
In addition to her proposed research, she will also present her ideas as to why simultaneous imaging of PET and MRI is so exciting.
Date: February 13, 2012
Professor of Radiology and Biomedical Imaging
School of Medicine
University of California, San Francisco, CA
Osteoarthritis (OA) is a degenerative disease that is characterized by cartilage thinning and compositional changes, and it is estimated that 20 million individuals in the United States are living with the disease with an annual cost of over 15 billion dollars.
The disease preferentially affects older (> 65 years) individuals, but with traumatic injury such as anterior cruciate ligament injury being a risk-factor, 1 in 20 working age (18-64 years old) adults report activities being limited by arthritis. Despite the recognition that 3D imaging is likely to provide important information regarding joint health, OA, and that biomechanics plays a role in OA and its progression, the translation and cross-correlation of these metrics have been limited.
The overall objective of this talk is to integrate cutting-edge quantitative imaging technologies, link the image-derived metrics to joint kinematics, kinetics, patient function, and translate the linkages found to the musculoskeletal clinic, thus affecting patient management and outcome.
Date: February 28, 2012
Associate Professor of Medical Biophysics
The Hebrew University of Jerusalem, Israel
Coherent low frequency fluctuations of the BOLD signal in resting state (rest-fcMRI) were shown to contain functional neuronal network information. Resting-state networks (RSN) exhibit positive correlations between the regions that constitute the network, suggesting a functional link between them. However, several RSNs were shown to have an inverse correlation between each other. The underlying physiological mechanisms and the relevance of negative correlations to neurobiology are not clear and are the subject of this study.
We compared human and rat rest-fcMRI data, making use of both the similarities (e.g., similar organization: cortical vs. non-cortical structures, inter-hemispheric symmetry etc.) and differences (e.g., different hemodynamic characteristics such as cardiac rates and spatial distances) between them. In addition, the fact that the rats' cortex is relatively unfolded, enables to minimize confounding effects of CSF and large blood vessels on the rest-fcMRI correlations.
We show that: (i) Negative correlations observed in rest-fcMRI reflect true physiological traits and are not the mere result of mathematical biases introduced by data analysis. (ii) At least two distinct mechanisms may underlay the appearance of negative correlations, reflecting the actual synchronization between regional neural activities on the one hand and their manifested BOLD signal responses on the other hand. (iii) The variant involvement of CBV in the hemodynamic responses of two different regions may introduce such negative correlations.
Date: March 13, 2012
Assistant Professor of Radiology
Center for Advanced Imaging Innovation and Research
New York University Langone Medical Center, NY
Iterative reconstruction techniques are currently getting popular in the MRI community because they enable the reconstruction of images from highly incomplete data, which can be exploited to skip acquisition steps and, thus, reduce scan time. This talk will first give a step-by-step introduction to the reconstruction technique and demonstrate how the technique can be applied for MRI data. The second part will discuss four application examples to illustrate the advantages over conventional methods. These advantages arise from two main components that inherently compensate for incompletely sampled data: First, the ability to incorporate prior knowledge about the object and, second, the ability to extend the signal modeling for advanced pulse sequences and acquisition techniques.
In the first example, it is shown that the higher sampling requirement for radial k-space sampling can be ameliorated with a constraint on the solution's total variation (TV), based on the assumption that many objects are piece-wise constant to some degree. Further, by extending the signal model to account for varying sensitivities of the receive coils, all channels can be processed simultaneously in a parallel imaging manner. In example 2, the concept is extended for radial fast spin-echo imaging where spokes with increasing T2 weighting are acquired along the echo train. When adding a spatial relaxation component to the signal calculation, the iterative approach is able to model these contrast inconsistencies and renders a proton-density map and a relaxation map directly from k-space, which can be used for fast T2 quantification.
In example 3, the signal model is extended to calculate the coil sensitivities jointly with the image content during the reconstruction, which offers improved parallel-imaging quality. Because in this way all sampled data is included for estimation of the coil profiles instead of only a few reference lines, the method yields artifact-free images in conditions where conventional parallel-imaging reconstructions already show spurious aliasing artifacts. Finally, the last example combines the above ideas with a temporal constraint on sequentially acquired time frames. For measurements with an optimized radial real-time sequence, the technique achieves temporal resolutions of up to 20 ms and yields cinematic insight into the human body.
Date: March 20, 2012
Assistant Professor of Medical Biophysics
Hospital for Sick Children
University of Toronto, Canada
A striking feature of any imaging study is just how much variability there is in brain shapes and sizes. This is especially the case in autism spectrum disorders, where the heterogeneity of the disease has resulted in a plethora of conflicting findings. In this talk, I will use brain imaging in the mouse, where we have much tighter control over genetics and the environment, to illustrate both how different genetic mutations related to autism can lead to similar behavioural outcomes yet divergent neuroanatomical alterations, as well as how the environment, learning, and memory can themselves change local brain shape.
Date: March 27, 2012
Assistant Professor of Radiology
The Bernard and Irene Schwartz Center for Biomedical Imaging
New York University Langone Medical Center, NY
Diffusion MRI is a powerful tool to characterize brain white matter microstructural and architectural tissue organization. Diffusional kurtosis imaging (DKI) is a clinically feasible diffusion MRI method that quantifies the non-Gaussian diffusion properties in biological tissue through estimation of the diffusional kurtosis. In this talk, I will present a specific white matter model that allows for a direct physical interpretation of the non-Gaussian signal in terms of specific white matter microstructural integrity metrics, such as the axonal water fraction and intra- and extra-axonal compartmental diffusivities.
Next, I will discuss how these white matter integrity markers may serve as specific and sensitive biomarkers useful to study both healthy development and a variety of pathological conditions. In particular, our initial findings in human ischemic stroke and Alzheimer's disease illustrate how investigating changes in these white matter metrics reveal new insights into the underlying pathophysiology.
Date: April 23, 2012
Associate Professor
Advanced Imaging Center
University of Texas Southwestern Medical Center, TX
We propose a procedure to measure global CMRO2 by combining several non-invasive measures obtained from MRI and pulse oximetry. A key technique of this procedure is a T2-Relaxation-Under-Spin-Tagging (TRUST) technique for the determination of global venous oxygenation. The TRUST MRI technique applies the spin labeling principle on the venous side and acquires control and labeled images, the subtraction of which yields pure venous blood signal. T2 value of the pure venous blood was then determined using non-selective T2-preparation pulses, minimizing the effect of flow on T2 estimation. Further technical considerations were made by using composite RF pulses and RF phase cycling in the T2-preparation.
We have measured Yv in both superior sagittal sinus (SSS) and internal jugular vein (IJV). Both measures yielded results consistent with expected venous values (50-75%) and, furthermore, a strong correlation was observed between them (P=0.0015), which is in agreement with the drainage path of venous blood. CMRO2 was estimated using TRUST and phase-contrast. Studies of intra-session and inter-session reproducibility of the CMRO2 measurement were conducted in seven subjects (26.4±4.0 years, 3 males and 4 females) and each subject underwent 5 sessions on different days. Intra-session and inter-session Coefficient of Variation (CoV) was 2.8±1.3% and 5.9±1.6%, respectively, suggesting a high reproducibility of this technique.
The dependence of CMRO2 on age was evaluated in our recent study. Average CMRO2 of typical 20-year-old subjects is approximately 164.1 µmol/100g/min and it increases with age at a rate of 2.6µmol/100g/min per decade, suggesting a reduced brain energy efficiency with age. We have also studied CMRO2 in an early stage of Alzheimer's Disease (AD) called Mild Cognitive Impairment (MCI) (Clinical Dementia Rating, CDR=0.5). In collaboration with the UTSW Alzheimer's Disease Center, we recruited 18 MCI patients (age 67±7 years) and 19 elderly controls (68±7 years). It was found that CMRO2 in MCI patients was 151.3±26.4 µmol/100g/min (mean±SD), which was significantly lower (P=0.04) than that of the control group (171.2±29.6 µmol/100g/min), suggesting that CMRO2 may be a sensitive marker for Alzheimer's Disease.
Date: June 19, 2012
Doctoral Candidate
University of Lyon-1, France
This work investigates the relative gain in sensitivity of a set of five histology coils designed in-house compared to a circularly polarized (CP) mouse head birdcage coil (L=29-mm x ID=28-mm). The dimensions of these coils were tailored to fit tissue sections ranging from 5-µm to 100-µm when mounted on either standard glass slides and/or coverslips. Our simulations and experimental measurements demonstrate that the sensitivity of this flat structure underperforms by a factor of two relative to the CP birdcage coil based on the expected gain in their filling factor ratios. Despite the inevitable dielectric losses attributed to this capacitor-like shape resonator, our results demonstrate that the overall net increase in filling factor overcomes the current leaks inherent to this structure.
Surprisingly, this leads to an enhancement in sensitivity of up to seven-fold for the smallest structure constructed (W=12-mm x L=24-mm x H=0.45-mm). Alternatively, the largest histology coil design (W=52-mm x L=48-mm x H=1.35-mm) enables two times wider radiofrequency flat coverage at equal sensitivity to the CP birdcage. Examples of tissue sections from both mouse organs and human specimens acquired during overnight experiments illustrate the level of detail observed and the near-perfect co-registration with optical microscopy.
Date: September 11, 2012
Post-Doctoral Fellow
The Bernard and Irene Schwartz Center for Biomedical Imaging
New York University Langone Medical Center, NY
Diffusion tensor imaging (DTI) provides biomarkers of tissue anisotropy and microstructure (principal diffusivities, mean diffusivity (MD) and fractional anisotropy (FA)), which have many applications in oriented biological tissue (e.g. neural fibers, renal tubules, muscle fibers). One route of acceleration of the multidirectional sampling required for DTI is multiple echoes. This presentation will describe progress in our use of this strategy to construct a dynamic DTI acquisition mode.
The first part of this talk will cover the feasibility of a two-scan Multiple Echo Diffusion Tensor Acquisition Technique (MEDITATE) on a clinical system for muscle DTI. In the MEDITATE-sequence, a pattern of diffusion gradients between the multiple RF-pulses encodes a train of echoes with each a different diffusion weighting and direction sufficient to estimate the 3D diffusion tensor. The work presented in this talk extends the original MEDITATE-approach, previously employed in preclinical settings, by exploiting longitudinal magnetization storage to reduce T2-weighting and optimizing a two-shot full tensor encoding within the clinical scanner hardware regime. Spin-warp phase encoding is used for image encoding. MEDITATE was tested on isotropic (agar gel) and anisotropic diffusion phantoms (asparagus), and in vivo skeletal muscle in healthy volunteers with cardiac-gating. Good quantitative agreement was found between diffusion eigenvalues, mean diffusivity, and fractional anisotropy derived from standard twice-refocused spin echo (TRSE) EPI-DTI and from several varieties of the MEDITATE sequence.
When combined with appropriate k-space trajectories or single voxel acquisition strategies, the accelerated encoding approach of MEDITATE may be used in clinical applications requiring time-sensitive acquisition of DTI parameters such as dynamical DTI in muscle. In that spirit, the second part of this talk will address the measurement of the exercise response of DTI biomarkers in skeletal muscle using dynamic MEDITATE, currently implemented using a line-scan image encoding approach. Finally, future plans and applications of the MEDITATE technique will be discussed.
Date: September 17, 2012
Professor of Radiology, Electrical and Biomedical Engineering
Center for Magnetic Resonance Research
University of Minnesota
The Radiofrequency (RF) transmit signal which stimulates the MR image signal, also deposits RF energy in the body resulting in heating. Because this RF heating can potentially result in pain, thermogenic tissue damage, and/or thermal stress to the human body, it must be better understood, predicted and monitored. Current MR safety practices however largely ignore tissue temperature as a safety metric in favor of the specific absorption rate (SAR) of RF energy deposition predicted from simple "standard" models of the human anatomy.
The problems with this "SAR" approach to RF safety are: 1. SAR by itself is not the cause of safety concerns, temperature is. 2.) SAR alone indicates neither the location nor the magnitude of thermal hot spots or overall body temperature. SAR based safety models consider only the electrodynamics, but not the thermodynamics or the physiology of humans being scanned. SAR is but one of six or more parameters in bioheat equations needed to predict temperature.
Modeling SAR only is therefore insufficient for predicting RF safety. By basing our safety metric on temperature rather than SAR however, we can not only be more safe, but in many cases we can safely use more RF power in our MRI scan protocols. This presentation will explore and explain SAR, RF Heating, and means to predict, monitor, and control them for MRI.
Date: September 25, 2012
Doctoral Candidate
The Sackler Institute of Graduate Biomedical Sciences
New York University School of Medicine, NY
Posttraumatic stress disorder is a prevalent psychiatric disorder in civil population and especially among combat veterans. The present study is about imaging characteristics of posttraumatic stress disorder in combat veterans. The neural characteristics of combat veterans who passed the diagnostic criteria of posttraumatic stress disorder were compared with those of combat veterans who did not meet the diagnostic criteria. The neural substrates are characterized by MRI in terms of amplitudes of spontaneous activity, temporal synchronization of spontaneous activity, properties and architecture of the neural networks. Results have suggested valuable characteristics such as spontaneous activity in the insula and precuneus, temporal synchronization between the amygdala and prefrontal cortex, disorganization of neural networks etc.
Date: October 23, 2012
Professor of Psychology and Neurosurgery
The Bernard and Irene Schwartz Center for Biomedical Imaging
University of Pittsburgh, PA
High Definition Fiber Tracking (HDFT) enables noninvasive MRI diffusion tracking of millimeter tracts over long distances accurately following from source to destination through tract crossings to detail axon projection fields of white matter tracts. Connection disorders are a major medical problem impacting tens of millions of patients with trauma (TBI), neuro-oncology, neurodegeneration (Alzheimer's) and developmental (autism) pathologies. HDFT involves mapping a million microtracts on a single individual with 3T MRI 257d DSI imaging with novel computation methods calculating directional axonal volume (dAV), tractography, and tract segmentation.
It creates a circuit diagram of the patient quantifying and visualizing the integrity of twenty brain white matter tracts. In a group TBI study the method produced high discriminant validity diagnosis of the anatomical basis of TBI showing nearly all TBI cases have visually and statistically clear damage to multiple tracts in mild TBI that was generally not detectable by previous methods. This provides the potential of definitive anatomically diagnosis of mild TBI and a foundation for a new ecology of personalized care and rehabilitation management.
Date: November 13, 2012
Associate Professor of Health Sciences and Technology
Athinoula A. Martinos Center for Biomedical Imaging
New York University Langone Medical Center, NY
Despite intense research in pTx hardware development there has been relatively little theoretical evaluation or optimization of pTx coil arrays, for example determining the benefit of increasing number of transmit channels. We quantify the performance of three pTx body arrays with 4, 8 and 16 channels by incorporating simultaneous constraints on global and local SAR as well as average and maximum forward input power. We analyze RF shimming and 2 spokes excitations in the torso at 3T and compare the tradeoff between excitation fidelity, pulse power metrics and local and global SAR.
Date: November 27, 2012
Doctoral Candidate
The Sackler Institute of Graduate Biomedical Sciences
New York University School of Medicine, NY
Growth in rare and expensive animal models of human disease has increased interest in non-destructive evaluation of tissue injury and/or response to therapy. Proton magnetic resonance spectroscopy (1H-MRS) is a valuable tool because of its unique ability to probe cellular metabolism and bioenergetics noninvasively and nondestructively. However, 1H resonances from metabolites of interest (other than water) typically occur in vivo at 104–105 orders of magnitude lower concentrations than water, leading to much lower sensitivity. To overcome this limitation, we utilize a three dimensional (3D) multivoxel 1H MRS technique, which localizes multiple tissue regions simultaneously, and collect spectra from hundreds of ‹‹1 cm3 voxels. Compared with single-voxel techniques, 3D 1H MRS benefits from improved (~15×) signal-to-noise ratio and higher spectral resolution. Acquiring 3D 1H MRS together with high resolution MRI may provide a quantitative, long-term solution to costly, invasive and destructive histology studies, and improve diagnostic sensitivity and specificity.
Over 50% of the million Americans infected with HIV will suffer milder, long-term HIV associated neurocognitive disorders (HAND). 1H MRS has proven valuable in detecting brain abnormalities in HAND patients, and in simian immunodeficiency virus (SIV) infected rhesus macaques, an excellent model system. Prior histology has demonstrated neuronal dysfunction in (sub)cortical gray and white matter, as well as glial activation. Based on these observations, we test the hypothesis that decreased N acetylaspartate, the MRS-observed marker for neuronal integrity, and increased glial markers: myo-inositol, choline and creatine, can be detected with 3D 1H-MRS both globally and regionally—in subcortical structures—using SIV-infected rhesus macaques.
Researchers at the Center for Biomedical Imaging at NYU Langone Health develop transformative imaging technologies to advance basic science and address unsolved clinical problems.
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