Early Knee Osteoarthritis Detection by Multi-Component T2 Mapping

This study investigates whether multi-component T₂ mapping, using bi-exponential (BE) and stretched-exponential (SE) models, enhances the early detection of knee osteoarthritis (OA) compared with the conventional mono-exponential (ME) approach. T₂ relaxation maps were derived from 26 patients with early-stage OA and 26 healthy controls. To minimize the influence of age-related cartilage changes, all model-derived parameters were adjusted for age prior to analysis. Quantitative T₂ parameters were extracted from six anatomically defined cartilage sub-regions to capture spatially heterogeneous tissue alterations characteristic of early OA. These parameters were then integrated using linear discriminant analysis to assess combined diagnostic performance. Global whole-cartilage analyses demonstrated limited discriminatory power across all models, with area under the receiver operating characteristic curve (AUC) values not exceeding 0.65, indicating that diffuse averaging obscures subtle, localized degeneration. In contrast, sub-regional analysis improved classification accuracy, highlighting the importance of regional assessment in early disease. Among the evaluated models, the BE-T₂ model showed the highest performance, achieving an AUC of 0.68, and marginally outperforming both the SE model (AUC = 0.60) and the ME model (AUC = 0.51). These findings suggest that multi-component T₂ mapping, particularly when applied at a sub-regional level, may offer improved sensitivity to early cartilage compositional changes. Overall, this approach shows strong potential as a noninvasive imaging biomarker for the early detection of knee OA.