Projects

Prediction Models of Knee Osteoarthritis Incidence and Progression using Deep Learning

This project develops and validates deep-learning models that analyze clinical and imaging data to predict individuals' five-year risk of knee osteoarthritis progression and total knee replacement, aiming to enable early intervention and personalized treatment.

AI   MSK

Advancing Multimodal AI for Breast Cancer Detection, Interpretation, and Risk Profiling

This research aims to develop multimodal artificial intelligence systems that enhance breast cancer detection and risk prediction, improve diagnostic consistency, reduce false positives and unnecessary biopsies, and enable precision screening and personalized prevention strategies.

AI   BREAST

Multiparametric Mapping of Knee Joint with Magnetic Resonance Fingerprinting

This project develops advanced magnetic resonance fingerprinting (MRF) methods enhanced with machine learning to improve the efficiency and robustness of MRI for early detection of osteoarthritis (OA) in the knee by identifying biochemical and structural changes before visible damage occurs.

MSK   QUANTITATIVE MRI

Genomic and Imaging Markers to Understand and Predict Progression of Joint Damage After Injury

This study combines genomic analysis and diffusion tensor imaging to identify predictive biomarkers for the risk of developing post-traumatic osteoarthritis (PTOA) following anterior cruciate ligament (ACL) injury in young adults, aiming to improve prevention and therapy development.

MSK

Understanding CSF Clearance in Aging and Alzheimer’s Brain Through Dynamic Sodium MRI

This study uses advanced MRI techniques to investigate how sleep and aging affect cerebrospinal fluid clearance in healthy individuals and Alzheimer’s patients, aiming to understand the glymphatic system’s role in brain health and disease progression.

AGING   BRAIN   X-NUCLEI
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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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