Statistical shape modelling versus linear scaling: Effects on predictions of hip joint centre location and muscle moment arms in people with hip osteoarthritis

Abstract

Marker-based dynamic functional or regression methods are used to compute joint centre locations that can be used to improve linear scaling of the pelvis in musculoskeletal models, although large errors have been reported using these methods. This study aimed to investigate if statistical shape models could improve prediction of the hip joint centre (HJC) location. The inclusion of complete pelvis imaging data from computed tomography (CT) was also explored to determine if free-form deformation techniques could further improve HJC estimates. Mean Euclidean distance errors were calculated between HJC from CT and estimates from shape modelling methods, and functional-and regression-based linear scaling approaches. The HJC of a generic musculoskeletal model was also perturbed to compute the root-mean squared error (RMSE) of the hip muscle moment arms between the reference HJC obtained from CT and the different scaling methods. Shape modelling without medical imaging data significantly reduced HJC location error estimates (11.4 ± 3.3mm) compared to functional (36.9 ± 17.5mm, p=<0.001) and regression (31.2 ± 15mm, p=<0.001) methods. The addition of complete pelvis imaging data to the shape modelling workflow further reduced HJC error estimates compared to no imaging (6.6 ± 3.1mm, p=0.002). Average RMSE were greatest for the hip flexor and extensor muscle groups using the functional (16.71mm and 8.87mm respectively) and regression methods (16.15mm and 9.97mm respectively). The effects on moment-arms were less substantial for the shape modelling methods, ranging from 0.05 to 3.2mm. Shape modelling methods improved HJC location and muscle moment-arm estimates compared to linear scaling of musculoskeletal models in patients with hip osteoarthritis.

Lower functioning patients demonstrate atypical hip joint loading before and following total hip arthroplasty for osteoarthritis

“…The Musculoskeletal Atlas Project Client 20 containing shape models of the pelvis and lower limbs was used to scale a generic musculoskeletal model (Gait2392) 21 . This approach has been shown to better reflect individual bony geometry compared to traditional linear scaling 15 . The workflow to scale a generic lower‐limb model has previously been described 15 but briefly, scale factors were computed based on the distances between markers on the unscaled model and corresponding embedded anatomical landmarks on the pelvis, femurs, and tibias from the shape model.…”

Modelling Human Locomotion to Inform Exercise Prescription for Osteoporosis

“…The body anatomy in the model can be informed by medical imaging [66][67][68] or extracted from population databases [69,70]. Statistical methods have been developed for improving the anatomical fidelity in the model using the limited set of information typically available in a clinical environment [71,72]. Real-time or near real-time numerical methods have been developed to predict muscle and joint force [73], bone strains [74,75] and strength [76].…”

A Systematic Review of the Associations Between Inverse Dynamics and Musculoskeletal Modeling to Investigate Joint Loading in a Clinical Environment

“…While medical images are included for scaling, the time for creating a participant-specific model increases (Davico et al, 2020). In marker based scaled models, errors in joint loading or muscle forces (Martelli et al, 2015b) can occur based on soft tissue artifacts (Wesseling et al, 2016) or incorrect defined joint centers (Martelli et al, 2015c;Kainz et al, 2017;Bahl et al, 2019). Moreover, the different definitions of the joints in case of degree of freedom (DoF) or muscle positioning have been shown to influence the outcome (Valente et al, 2014(Valente et al, , 2015.…”

“…Previous studies found that the different scaling approaches, e.g., body mass based scaling, scaling based on shape modeling or linear scaling affect the outcome of MSK modeling (Kainz et al, 2017;Bahl et al, 2019). In general, scaling based on medical images or with the inclusion of calculated joint centers into the scaling process improves the accuracy of the calculation of the hip joint center location compared to scaling with surface markers alone (Kainz et al, 2017;Bahl et al, 2019).…”