Zonal variation of MRI-measurable parameters classifies cartilage degradation

Osteoarthritis (OA) is a degenerative joint disease resulting in the deterioration of articular cartilage, a tissue with minimal ability to self-repair. Early diagnosis of OA with non-invasive imaging techniques such as magnetic resonance imaging (MRI) could provide an opportunity to intervene and slow or reverse this degeneration process. This study examines the classification of degradation states using MRI measurements. Enzymatic degradation was used to specifically target proteoglycans alone, collagen alone and both cartilage components sequentially. The resulting degradation was evaluated using MRI imaging techniques (T₁, T₂, diffusion tensor imaging, and gadolinium enhanced T₁) and derived measures of water, glycosaminoglycan and collagen content. We compared the classification ability of full thickness averages of these parameters with zonal averages (superficial, medial, and deep). Finally, we determined minimum variables sets to identify the smallest number of variables that allowed for complete separation of all degradation groups and ranked them by impact on the separation. Zonal analysis was much more sensitive than full thickness averages and allowed perfect separation of all four groups. Superficial zone cartilage was more sensitive to enzymatic degradation than the medial or deep zone, or the full thickness average. Variable ranking consistently identified collagen content and organization as the most impactful variables in the classification algorithm. The aim of this study is to classify cartilage degradation using only non-invasive MRI parameters that could be applied to OA diagnosis. Our results highlight the importance of zonal variation in the diagnosis of cartilage degeneration. Our novel, non-invasive collagen content measurement was crucial for complete separation of degraded groups from control cartilage. These findings have significant implications for clinical cartilage MRI for disease diagnosis.