Abstract
BackgroundBone shapes strongly influence force and moment predictions of kinematic and musculoskeletal models used in motion analysis. The precise determination of joint reference frames is essential for accurate predictions. Since clinical motion analysis typically does not include medical imaging, from which bone shapes may be obtained, scaling methods using reference subjects to create subject-specific bone geometries are widely used. Research questionThis study investigated if lower limb bone shape predictions from skin-based measurements, utilising an underlying statistical shape model (SSM) that corrects for soft tissue artefacts in digitisation, can be used to improve conventional linear scaling methods of bone geometries. MethodsSSMs created from 35 healthy adult femurs and tibiae/fibulae were used to reconstruct bone shapes by minimising the distance between anatomical landmarks on the models and those digitised in the motion laboratory or on medical images. Soft tissue artefacts were quantified from magnetic resonance images and then used to predict distances between landmarks digitised on the skin surface and bone. Reconstruction results were compared to linearly scaled models by measuring root mean squared distances to segmented surfaces, calculating differences of commonly used anatomical measures and the errors in the prediction of the hip joint centre. ResultsSSM reconstructed surface predictions from varying landmark sets from skin and bone landmarks were more accurate compared to linear scaling methods (2.60–2.95 mm vs. 3.66–3.87 mm median error; p < 0.05). No significant differences were found between SSM reconstructions from bony landmarks and SSM reconstructions from digitised landmarks obtained in the motion lab and therefore reconstructions using skin landmarks are as accurate as reconstructions from landmarks obtained from medical images. SignificanceThese results indicate that SSM reconstructions can be used to increase the accuracy in obtaining bone shapes from surface digitised experimental data acquired in motion lab environments.
Highlights
Knowledge of the shapes of the underlying skeletal anatomy is important for the accurate estimation of joint kinematics, kinetics and the prediction of muscle forces
The aim of this study was to test the hypothesis that lower limb bone shape predictions from skin-based measurements, utilising an underlying statistical shape model (SSM) that corrects for soft tissue artefacts in digitisation, are more accurate than conventional linear scaling of bone geometries
The median root mean square error (RMSE) of the reconstructed surfaces from landmarks digitised on the bone and skin surfaces were 2.66 mm compared to 2.60 mm for the femur and 2.88 mm compared to 2.90 mm for the tibia; the errors of the hip joint centre (HJC) locations were 13.82 mm compared to 16.10 mm
Summary
Knowledge of the shapes of the underlying skeletal anatomy is important for the accurate estimation of joint kinematics, kinetics and the prediction of muscle forces. Optical marker-based motion capture enables the quantification of kinematics and, in combination with inverse dynamics and optimisation approaches to determine muscle forces, facilitates the estimation of joint torques and joint contact forces. These calculations are sensitive to estimations of joint centres. Since clinical motion analysis typically does not include medical imaging, from which bone shapes may be obtained, scaling methods using reference subjects to create subject-specific bone geometries are widely used. Research question: This study investigated if lower limb bone shape predictions from skin-based measurements, utilising an underlying statistical shape model (SSM) that corrects for soft tissue artefacts in digitisation, can be used to improve conventional linear scaling methods of bone geometries. Significance: These results indicate that SSM reconstructions can be used to increase the accuracy in obtaining bone shapes from surface digitised experimental data acquired in motion lab environments
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