Koller, W. | bioRxiv Science

bioRxiv2025

A Workflow to Create Personalised Musculoskeletal Models Based on Magnetic Resonance Images

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. Author SummaryQuestions about healthy or pathological movement patterns in humans--critical for injury prevention, rehabilitation, and sports performance--are often explored with the help of musculoskeletal modelling. This approach uses a priori defined generic models of the human musculoskeletal system to study joint moments, muscle activation patterns and joint contact forces. Typically, a generic model is scaled to match the participants dimensions linearly, which does not allow for an accurate representation of their bone and muscle morphology. We developed a semi-automatic workflow to creating magnetic resonance imaging-based personalisation that does not require bone segmentation but closely matches individual geometry with the help of a non-linear fitting function. By comparing magnetic resonance imaging-based and generic-scaled models in eight individuals, we show systematic bias inherent to one of the most popular musculoskeletal models and demonstrate the importance of model personalisation for healthy adults. Our personalisation pipeline is openly available and easy to set up, which will facilitate musculoskeletal modelling studies based on highly personalised models in clinical and research settings, potentially improving treatment planning and biomechanical assessments in the future.

biophysics↗

bioRxiv2024

Femoral bone growth predictions based on personalized multi-scale simulations: Validation and sensitivity analysis of a mechanobiological model

Musculoskeletal function is pivotal to long-term health. However, various patient groups develop torsional deformities, leading to clinical, functional problems. Understanding the interplay between movement pattern, bone loading and growth is crucial for improving the functional mobility of these patients and preserving long-term health. Multi-scale simulations in combination with a mechanobiological bone growth model have been used to estimate bone loads and predict femoral growth trends based on cross-sectional data. The lack of longitudinal data in previous studies hindered refinements of the mechanobiological model and validation of subject-specific growth predictions, thereby limiting clinical applications. This study aimed to validate the growth predictions using magnetic resonance images and motion capture data - collected longitudinally - from ten growing children. Additionally, a sensitivity analysis was conducted to refine model parameters. A linear regression model based on physical activity information, anthropometric data, and predictions from the refined mechanobiological model explained 70% of femoral anteversion development. Notably, the direction of femoral development was accurately predicted in 18 out of 20 femurs, suggesting that growth predictions could help to revolutionize treatment strategies for torsional deformities. Statements and DeclarationsThe authors have no relevant financial or non-financial interests to disclose.

pathology↗

bioRxiv2023

Trial-to-trial similarity and distinctness of muscle synergy activation coefficients increases during learning and with a higher level of movement proficiency

Muscle synergy analyses are used to increase our understanding of motor control. Spatially fixed synergy vectors coordinate multiple co-active muscles through activation commands, known as activation coefficients. To better understand motor learning, it is crucial to know how synergy recruitment varies during a learning task and different levels of movement proficiency. Within one session participants walked on a line, a beam, and learned to walk on a tightrope - tasks that represent different levels of proficiency. Muscle synergies were extracted over all conditions and the number of synergies was determined through the knee-point of the total variance accounted for (tVAF) curve. We found that the tVAF of one synergy decreased with task proficiency (line < beam < tightrope). Additionally, trial-to-trial similarity and distinctness of synergy activation coefficients increased with proficiency and after a learning process. We conclude that precise adjustment and refinement of synergy activation coefficients play a crucial role in motor learning.

neuroscience↗