This project focuses on the development of magnetic resonance fingerprinting (MRF) methods for magnetic resonance imaging (MRI) of the knee in order to improve early detection of osteoarthritis (OA).
Knee OA is a common degenerative joint disease that causes breakdown of knee cartilage and leads to biochemical, structural, and morphological changes. There is currently no cure for OA. Early detection of cartilage degeneration, critical for interventions and research, requires identifying changes in vivo before visible damage occurs. Quantitative MRI metrics of T1, T2, and T1rho relaxation can detect changes in articular extracellular matrix. However, existing methods are slow and sensitive to noise and magnetic field variations.
MRF is a relatively new technique that makes use of dynamic signal patterns and is capable of simultaneously estimating multiple MRI parameters, an approach that makes scans more efficient and more robust for musculoskeletal applications. In this project, we enhance MRF with advanced machine learning techniques to improve early detection of OA in the knee.
We acknowledge support from the following NIH grant: NIH-R01 AR076985.
Researchers at the Center for Biomedical Imaging at NYU Langone Health develop transformative imaging technologies to advance basic science and address unsolved clinical problems.
660 First Avenue
4th Floor
New York, NY 10016
© Center for Biomedical Imaging (CBI). All rights reserved.
This content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, NYU Langone Health, or New York University.
