Esrafilian Amir, Chandra Shekhar S, Gatti Anthony A, Nissi Mikko J, Mustonen Anne-Mari, Saisanen Laura, Reijonen Jusa, Nieminen Petteri, Julkunen Petro, Toyras Juha, Saxby David J, Lloyd David G, Korhonen Rami K
IEEE Trans Biomed Eng. 2025 Jan;72(1):56-69. doi: 10.1109/TBME.2024.3438272. Epub 2025 Jan 15.
To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline.
Magnetic resonance images (MRIs) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, and patella), menisci, and major knee ligaments. Two different MRI sequences were used to broaden applicability. Next, we created MSK-FE models of an unseen dataset using two MSK-FE modeling pipelines: template-based and auto-meshing. MSK models had personalized knee geometries with multi-degree-of-freedom elastic foundation contacts. FE models used fibril-reinforced poroviscoelastic swelling material models for cartilages and menisci.
Volumes of knee bones, cartilages, and menisci did not significantly differ (p>0.05) across MRI sequences. MSK models estimated secondary knee kinematics during passive knee flexion tests consistent with in vivo and simulation-based values from the literature. Between the template-based and auto-meshing FE models, estimated cartilage mechanics often differed significantly (p<0.05), though differences were <15% (considering peaks during walking), i.e., <1.5 MPa for maximum principal stress, <1 percentage point for collagen fibril strain, and <3 percentage points for maximum shear strain.
The template-based modeling provided a more rapid and robust tool than the auto-meshing approach, while the estimated knee biomechanics were comparable. Nonetheless, the auto-meshing approach might provide more accurate estimates in subjects with distinct knee irregularities, e.g., cartilage lesions.
The MSK-FE modeling tool provides a rapid, easy-to-use, and robust approach for investigating task- and person-specific mechanical responses of the knee cartilage and menisci, holding significant promise, e.g., in personalized rehabilitation planning.
开发并评估一种自动且稳健的膝关节肌肉骨骼有限元（MSK - FE）建模流程。
利用磁共振成像（MRI）训练nnU - Net网络，用于膝关节骨骼（股骨、胫骨、髌骨和腓骨）、软骨（股骨、胫骨和髌骨）、半月板和主要膝关节韧带的自动分割。使用两种不同的MRI序列以拓宽适用性。接下来，我们使用两种MSK - FE建模流程为一个未见数据集创建MSK - FE模型：基于模板的和自动网格划分的。MSK模型具有个性化的膝关节几何形状以及多自由度弹性基础接触。有限元模型对软骨和半月板使用纤维增强的多孔粘弹性肿胀材料模型。
膝关节骨骼、软骨和半月板的体积在不同MRI序列间无显著差异（p>0.05）。MSK模型在被动膝关节屈曲测试中估计的膝关节二次运动学与文献中的体内和基于模拟的值一致。在基于模板的和自动网格划分的有限元模型之间，估计的软骨力学特性通常有显著差异（p<0.05），不过差异<15%（考虑步行期间的峰值），即最大主应力<1.5 MPa，胶原纤维应变<1个百分点，最大剪切应变<3个百分点。
基于模板的建模提供了一种比自动网格划分方法更快速且稳健的工具，而估计的膝关节生物力学特性相当。尽管如此，自动网格划分方法可能在有明显膝关节不规则情况（如软骨损伤）的受试者中提供更准确的估计。
MSK - FE建模工具为研究膝关节软骨和半月板的任务及个体特异性力学响应提供了一种快速、易用且稳健的方法，在个性化康复计划等方面具有重大前景。
