MR-Imaging in Osteoarthritis: Current Standard of Practice and Future Outlook

Abstract

Osteoarthritis (OA) is a common degenerative joint disease that affects millions of people worldwide. Magnetic resonance imaging (MRI) has emerged as a powerful tool for the evaluation and monitoring of OA due to its ability to visualize soft tissues and bone with high resolution. This review aims to provide an overview of the current state of MRI in OA, with a special focus on the knee, including protocol recommendations for clinical and research settings. Furthermore, new developments in the field of musculoskeletal MRI are highlighted in this review. These include compositional MRI techniques, such as T2 mapping and T1rho imaging, which can provide additional important information about the biochemical composition of cartilage and other joint tissues. In addition, this review discusses semiquantitative joint assessment based on MRI findings, which is a widely used method for evaluating OA severity and progression in the knee. We analyze the most common scoring methods and discuss potential benefits. Techniques to reduce acquisition times and the potential impact of deep learning in MR imaging for OA are also discussed, as these technological advances may impact clinical routine in the future.

Evaluation of the consistency of the MRI- based AI segmentation cartilage model using the natural tibial plateau cartilage

“…Recently, deep learning (DL) models of artificial intelligence (AI), specifically convolutional neural networks (CNNs), have emerged as a novel approach for knee cartilage segmentation and KOA diagnosis [ 13 ]. Unlike traditional hand-crafted strategies, DL models automatically learn features through multiple layers and numerous parameters [ 14 ]. Prasoon et al [ 15 ] developed three 2D CNNs, known as multi-planar CNNs.…”