Best methods and data to reconstruct paediatric lower limb bones for musculoskeletal modelling

Abstract

In biomechanical simulations, generic linearly scaled musculoskeletal anatomies are commonly used to represent children, often neglecting or oversimplifying subject-specific features that may affect model estimates. Inappropriate bone sizing may influence joint angles due to erroneous joint centre identification. Alternatively, subject-specific image-based musculoskeletal models allow for more realistic representations of the skeletal system. To this end, statistical shape modelling (SSM) and morphing techniques may help to reconstruct bones rapidly and accurately. Specifically, the musculoskeletal atlas project (MAP) Client, which employs magnetic resonance imaging (MRI) and/or motion capture data to inform SSM and nonrigid morphing techniques, proved able to accurately reconstruct adult pelvis and femur bones. Nonetheless, to date, the above methods have never been applied to paediatric data. In this study, pelvis, femurs and tibiofibular bones of 18 typically developing children were reconstructed using the MAP Client. Ten different combinations of SSM and morphing techniques, i.e. pipelines, were developed. Generic bone geometries from the gait2392 OpenSim model were linearly scaled for comparisons. Jaccard index, root mean square distance error and Hausdorff distance were computed to quantify reconstruction accuracy. For the pelvis bone, colour maps were produced to identify areas prone to inaccuracies and hip joint centres (HJC) location was compared. Finally, per cent difference between MRI- and MAP-measured left-to-right HJC distances was computed. Pipelines informed by MRI data, alone or in combination with motion capture data, accurately reconstructed paediatric lower limb bones (i.e. Jaccard index > 0.8). Scaled OpenSim geometries provided the least accurate reconstructions. Principal component-based scaling methods produced size-dependent results, which were worse for smaller children.status: publishe