Projects

Data-Driven Learning Framework for Fast Quantitative Knee Joint Mapping

This project leverages advanced machine learning and deep learning to optimize T2 and T1rho mapping for knee osteoarthritis (OA), aiming to accelerate MRI techniques and enable earlier detection of cartilage degeneration.

AI   MSK   QUANTITATIVE MRI

Longitudinal Single-Center Study with Rapid Quantitative Assessment of Knee Joint with Compressed Sensing

This study uses compressive sensing (CS) techniques to accelerate knee MRI and track osteoarthritis (OA) progression by measuring T2 and T1rho relaxation times, enabling faster detection of cartilage degeneration and changes associated with advancing disease.

MSK   QUANTITATIVE MRI

Imaging and Therapeutic Targeting of Tissue Crosstalk in the Injured Knee

This project investigates the interplay between inflammation and apoptosis in post-traumatic osteoarthritis (PTOA) using in vitro and in vivo methods to develop therapeutic strategies that target both pathways and prevent disease progression.

MOLECULAR IMAGING   MSK

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

Quantitative Methods for the Evaluation of Femoroacetabular Impingement Using Magnetic Resonance Imaging

This project aims to develop advanced, translation-ready quantitative MRI and radiomics methods to improve early diagnosis and evaluation of femoroacetabular impingement (FAI), enabling more reliable and reproducible assessments than standard radiologic approaches.

MSK   QUANTITATIVE MRI   RADIOMICS
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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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