Projects

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

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

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

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

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
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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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