Projects

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

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

FireVoxel: Interactive Software for Multi-Modality Analysis of Dynamic Medical Images

FireVoxel is a freely distributed medical imaging analysis software developed at NYU Langone that provides powerful quantitative tools—especially for dynamic abdominal and genitourinary studies—and has become widely adopted in MRI research worldwide.

QUANTITATIVE MRI   SOFTWARE

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

Noninvasive Estimation of Electrical Properties from Magnetic Resonance Measurements

This project develops a three-pronged strategy—combining global inverse modeling, physics-informed neural networks, and vision transformers—to generate high-resolution maps of tissue electrical properties using MRI, with broad applications in medical imaging and treatment.

QUANTITATIVE MRI   SIMULATIONS
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