Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models
File(s)
Accepted version
Published version
Author(s)
Type
Journal Article
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 26 mm; 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.
Date Issued
2016-09-14
Date Acceptance
2016-09-07
Citation
Journal of Biomechanics, 2016, 49 (14), pp.3576-3581
ISSN
1873-2380
Publisher
Elsevier
Start Page
3576
End Page
3581
Journal / Book Title
Journal of Biomechanics
Volume
49
Issue
14
Copyright Statement
© 2016 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)
(http://creativecommons.org/licenses/by/4.0/)
License URL
Sponsor
Wellcome Trust
Engineering & Physical Science Research Council (E
Grant Number
088844/Z/09/Z
EP/K503733/1
Subjects
Lower extremity, Musculoskeletal model, Scaling methods, Subject-specific modelling, Statistical shape modelling
Publication Status
Published