Compares 3D MRI-modelling with laser scanning to evaluate elbow cartilage's impact on joint stress distribution.
Aims To construct and validate a 3D MRI-based cartilage model of the elbow joint by comparing it with laser-scanned models and to assess how cartilage morphology influences intra-articular stress distribution through finite element analysis (FEA). Methods Seven formalin-fixed cadaveric elbows were scanned using high-resolution MRI (voxel size: 0.375 × 0.375 × 0.400 mm), with sequences selected based on the contrast-to-noise ratio. Cartilage models of the distal humerus, radial head, and proximal ulna were reconstructed and validated against laser-scanned reference models. Validation metrics included cartilage thickness differences, surface-to-surface distances, and Dice similarity coefficients. Subsequently, FEA was performed using models with and without cartilage, under physiological loading, to evaluate the intra-articular stress distribution. Results MRI-based and laser-scanned models showed similar cartilage thickness distributions, with differences within or close to the imaging resolution. The median cartilage thicknesses measured using the MRI-based and laser-scanned models were 1.15 mm (IQR 0.97 to 1.54) and 1.25 mm (IQR 1.14 to 1.29) in the distal humerus, 1.15 mm (IQR 0.97 to 1.18) and 1.03 mm (IQR 1.01 to 1.14) in the radial head, and 1.06 mm (IQR 0.93 to 1.21) and 1.05 mm (IQR 1.00 to 1.10) in the proximal ulna, respectively. The surface-to-surface root mean square distances were < 0.40 mm, and the Dice coefficients exceeded 0.96. FEA revealed physiologically plausible stress concentration patterns at anatomical contact areas only in models incorporating the cartilage. Conclusion The 3D MRI-based cartilage models have high geometrical accuracy and biomechanical validity. These models offer a noninvasive tool for assessing cartilage morphology and joint mechanics, and may provide a foundation for future studies investigating the pathophysiology of elbow osteoarthritis. Cite this article: Bone Joint Res 2026;15(7):811–821.
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