Abstract
Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank – up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods.
Highlights
Helm (2004) found that more than 50% of the scapula muscle paths could be reconstructed with high accuracy using a nonlinear scaling method
The bone surfaces were reconstructed with a maximal average error of 1.672.0 mm for femur and tibia/fibula bone using the first 50 PMVs
Muscle points and landmarks of a reference morphed to the underlying subject geometry using the three different scaling methods showed similar significant differences between the methods (Table 5; p o0.05 for all)
Summary
Helm (2004) found that more than 50% of the scapula muscle paths could be reconstructed with high accuracy using a nonlinear scaling method. A common limitation of non-linear scaling methods is the need for either segmented bone surfaces or medical images of the entire limb. This limits the applicability of these methods for musculoskeletal analysis when image data are not available. Statistical shape models (SSMs) allow accurate reconstruction of geometries from sparse data obtained with basic clinical imaging techniques. These include reconstruction of a 3D shape from a single X-ray (Zheng and Nolte, 2006) or stereo X-ray (Baka et al, 2011) as well as the prediction of a healthy from a pathological shape from 3D scans of joint regions (Rajamani et al, 2004, 2005). Linking together bone morphing using reconstructions and geometrical models of muscle paths has not been attempted previously
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