Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models

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.

Improving Musculoskeletal Model Scaling Using an Anatomical Atlas: The Importance of Gender and Anthropometric Similarity to Quantify Joint Reaction Forces

“…This indicates that the linear scaling based on the gender and anthropometric similarity may not be adequate, especially for the hip joint force prediction. Recent studies have demonstrated a better estimation of muscle attachment sites by applying a morphing technique to the bone surface when compared to linear scaling [54], [55]. This technique could in future be applied to generate a larger, population-based dataset of subject-specific musculoskeletal models.…”

Automatic Generation of Personalised Skeletal Models of the Lower Limb from Three-Dimensional Bone Geometries

“…Incomplete bone models, however, may represent an obstacle to the identification of the muscle attachments when the aim is generating a complete musculoskeletal model. This issue can be solved by combining the STAPLE toolbox with a statistical shape workflow to reconstruct entire bone geometries from sparse datasets (Nolte et al, 2016;Suwarganda et al, 2019). It is worth mentioning that, although statistical shape modelling workflows present the advantage of reconstructing bone geometries from sparse segmentations or even skin landmarks, bone models from medical image segmentation still provide the most accurate estimations of joint parameters; for example, median root-mean-squared errors up to 11.09 mm and larger than 13.8 mm (Nolte et al, 2020) have been reported in the identification of the centre of the femoral head using statistical shape models.…”

“…Statistical shape modelling workflows have recently demonstrated high potential for reconstructing bone geometries from sparse anatomical datasets (Davico et al, 2019;Nolte et al, 2016;Suwarganda et al, 2019) and landmarks digitized in the gait lab (Nolte et al, 2020;Zhang et al, 2016), but to the best of the authors' knowledge they do not yet offer methods to generate articulated skeletal models of the complete lower limb. The bone reconstructions are limited to the long bones (Nolte et al, 2020;Nolte et al, 2016) or omit the talus and foot bones (Davico et al, 2019;Suwarganda et al, 2019;Zhang et al, 2016), and in musculoskeletal modelling contexts they have been employed to perform non-linear scaling of pre-existing muscle attachments (Nolte et al, 2016) with scarce focus towards joint modelling. Hence, a comprehensive approach to generate entire lower limb models from personalised bone geometries is still missing.…”

Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model

“…Computational approaches to study the correlation between morphological features and functional or pathological conditions of bony surfaces using SSM have been emerging in the literature, with impacts in biomechanics, especially for kinematic and dynamic analysis (Rao et al, 2013;Smoger et al, 2015;Nolte et al, 2016;Zhang et al, 2016;Hollenbeck et al, 2018;Clouthier et al, 2019), and clinics, especially for diagnostic and surgical interests MoV/p value HKA 5 (p = 0.0001), 7 (p = 0.03), 17 (p = 0.02), 18 (p = 0.03) FVV 5 (p = 0.001), 10 (p = 0.01), 17 (p = 0.01) IER 2 (p = 0.02) TVV 11 (p = 0.008), 14 (p = 0.01), 16 (p = 0.01), 17 (p = 0.03) TS 5 (p = 0.02) (Neogi et al, 2013;Peloquin et al, 2014;Mutsvangwa et al, 2015;Cerveri et al, 2018). In particular, three studies addressed the relation between SSM parameters and knee kinematics by focusing on the link between the morphological variability of the bones and tibio-femoral alignment modifications (Rao et al, 2013;Smoger et al, 2015;Clouthier et al, 2019).…”