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.
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.
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.