Getting Cartilage Thickness Measurements Right: A Systematic Inter-Method Comparison Using MRI Data from the Osteoarthritis Initiative.

OBJECTIVE: Magnetic resonance imaging is the standard imaging modality to assess articular cartilage. As the imaging surrogate of degenerative joint disease, cartilage thickness is commonly quantified after tissue segmentation. In lack of a standard method, this study systematically compared five methods for automatic cartilage thickness measurements across the knee joint and as a function of region and sub-region: 3D mesh normals (3D-MN), 3D nearest neighbors (3D-NN), 3D ray tracing (3D-RT), 2D centerline normals (2D-CN), and 2D surface normals (2D-SN). DESIGN: Based on the manually segmented femoral and tibial cartilage of 507 human knee joints, mean cartilage thickness was computed for the entire femorotibial joint, 4 joint regions, and 20 subregions using these methods. Inter-method comparisons of mean cartilage thickness and computation times were performed by one-way analysis of variance (ANOVA), Bland-Altman analyses and Lin's concordance correlation coefficient (CCC). RESULTS: Mean inter-method differences in cartilage thickness were significant in nearly all subregions (P < 0.001). By trend, mean differences were smallest between 3D-MN and 2D-SN in most (sub)regions, which is also reflected by highest quantitative inter-method agreement and CCCs. 3D-RT was prone to severe overestimation of up to 2.5 mm. 3D-MN, 3D-NN, and 2D-SN required mean processing times of ≤5.3 s per joint and were thus similarly efficient, whereas the time demand of 2D-CN and 3D-RT was much larger at 133 ± 29 and 351 ± 10 s per joint (P < 0.001). CONCLUSIONS: In automatic cartilage thickness determination, quantification accuracy and computational burden are largely affected by the underlying method. Mesh and surface normals or nearest neighbor searches should be used because they accurately capture variable geometries while being time-efficient.

Varus stress MRI in the refined assessment of the posterolateral corner of the knee joint.

Magnetic resonance imaging (MRI) is commonly used to assess traumatic and non-traumatic conditions of the knee. Due to its complex and variable anatomy, the posterolateral corner (PLC)-often referred to as the joint's dark side-remains diagnostically challenging. We aimed to render the diagnostic evaluation of the PLC more functional by combining MRI, varus loading, and image post-processing in a model of graded PLC injury that used sequential transections of the lateral collateral ligament, popliteus tendon, popliteofibular ligament, and anterior cruciate ligament. Ten human cadaveric knee joint specimens underwent imaging in each condition as above, and both unloaded and loaded using an MR-compatible device that standardized loading (of 147 N) and position (at 30° flexion). Following manual segmentation, 3D joint models were used to computationally measure lateral joint space opening for each specimen, configuration, and condition, while manual measurements provided the reference standard. With more extensive ligament deficiency and loading, lateral joint spaces increased significantly. In conclusion, varus stress MRI allows comprehensive PLC evaluation concerning structural integrity and associated functional capacity. Beyond providing normative values of lateral compartment opening, this study has potential implications for diagnostic and surgical decision-making and treatment monitoring in PLC injuries.