Musculoskeletal simulations typically rely on generic models that may not accurately represent individual anatomy. While personalisation based on medical images can improve model accuracy, current approaches often require time-consuming workflows to create these models. We present a semi-automatic workflow for creating personalised musculoskeletal models based on magnetic resonance imaging (MRI) that does not require bone segmentation. Our workflow uses 3D Slicer and Python scripts employing Thin-Plate Spline transformation to map 106 homologous landmarks from generic models onto participants' anatomy. Generic-scaled and MRI-based models were created for eight healthy participants, and simulations were performed using the participants' 3D motion capture data. MRI-based models were compared with generic-scaled models through principal component analysis, and joint kinematics and joint contact forces were analysed between both modelling approaches. Clear geometric differences existed between model types, with MRI-based models showing wider pelvises and different femur/tibia proportions. Unlike generic models, MRI-based male and female models displayed systematic differences. Despite anatomical discrepancies, joint kinematics were similar between models of the same individual, except for pelvis tilt. Muscle moment arms were generally aligned with published data from cadaver studies. MRI-based models consistently produced higher joint contact forces with greater inter-individual variation, particularly at knee joints, compared to generic-scaled models. The proposed workflow simplifies MRI-based model creation while revealing significant sensitivity of joint contact forces to individual morphology, highlighting the importance of personalisation for biomechanical analyses.
Stansfield et al. (Sun,) studied this question.