@article{Nolte:2016:10.1016/j.jbiomech.2016.09.005,
author = {Nolte, D and Tsang, CK and Zhang, KY and Ding, Z and Kedgley, AE and Bull, AMJ},
doi = {10.1016/j.jbiomech.2016.09.005},
journal = {Journal of Biomechanics},
pages = {3576--3581},
title = {Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models},
url = {http://dx.doi.org/10.1016/j.jbiomech.2016.09.005},
volume = {49},
year = {2016}
}

Download

TY  - JOUR
AB  - 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.
AU  - Nolte,D
AU  - Tsang,CK
AU  - Zhang,KY
AU  - Ding,Z
AU  - Kedgley,AE
AU  - Bull,AMJ
DO  - 10.1016/j.jbiomech.2016.09.005
EP  - 3581
PY  - 2016///
SN  - 1873-2380
SP  - 3576
TI  - Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models
T2  - Journal of Biomechanics
UR  - http://dx.doi.org/10.1016/j.jbiomech.2016.09.005
UR  - http://hdl.handle.net/10044/1/40120
VL  - 49
ER  - 

Download