Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models

• Published in: Journal of anatomy Vol. 237; no. 5; pp. 941 - 959
• Main Authors: Charles, James P., Grant, Barbara, D’Août, Kristiaan, Bates, Karl T.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.11.2020; John Wiley and Sons Inc
• Subjects: Accuracy; Adult; Aged, 80 and over; Ankle; biomechanical modelling; Diffusion Tensor Imaging; Female; fibre lengths; Humans; Magnetic resonance imaging; Male; muscle torques; muscle volumes; Muscle, Skeletal - diagnostic imaging; Muscle, Skeletal - physiology; Musculoskeletal system; Original Paper; Original Papers; Patient-Specific Modeling; Torque; muscle volumes; magnetic resonance imaging; muscle torques; biomechanical modelling; fibre lengths
• ISSN: 0021-8782; 1469-7580; 1469-7580
• DOI: 10.1111/joa.13261

Musculoskeletal modelling is an important platform on which to study the biomechanics of morphological structures in vertebrates and is widely used in clinical, zoological and palaeontological fields. The popularity of this approach stems from the potential to non‐invasively quantify biologically important but difficult‐to‐measure functional parameters. However, while it is known that model predictions are highly sensitive to input values, it is standard practice to build models by combining musculoskeletal data from different sources resulting in ‘generic’ models for a given species. At present, there are little quantitative data on how merging disparate anatomical data in models impacts the accuracy of these functional predictions. This issue is addressed herein by quantifying the accuracy of both subject‐specific human limb models containing individualised muscle force‐generating properties and models built using generic properties from both elderly and young individuals, relative to experimental muscle torques obtained from an isokinetic dynamometer. The results show that subject‐specific models predict isokinetic muscle torques to a greater degree of accuracy than generic models at the ankle (root‐mean‐squared error – 7.9% vs. 49.3% in elderly anatomy‐based models), knee (13.2% vs. 57.3%) and hip (21.9% vs. 32.8%). These results have important implications for the choice of musculoskeletal properties in future modelling studies, and the relatively high level of accuracy achieved in the subject‐specific models suggests that such models can potentially address questions about inter‐subject variations of muscle functions. However, despite relatively high levels of overall accuracy, models built using averaged generic muscle architecture data from young, healthy individuals may lack the resolution and accuracy required to study such differences between individuals, at least in certain circumstances. The results do not wholly discourage the continued use of averaged generic data in musculoskeletal modelling studies but do emphasise the need for to maximise the accuracy of input values if studying intra‐species form–function relationships in the musculoskeletal system. Diffusion tensor imaging (DTI) allows for the accurate visualisation and measurement of important muscle architecture factors such as muscle fibre lengths in vivo, but the accuracy of these data derived from this method has not been tested in a functional context, such as in subject‐specific musculoskeletal models. This study aimed to test the relative accuracy of personalised muscle architecture data from DTI and MRI compared to more generic data at predicting muscle torques in subject‐specific musculoskeletal models. It was found that, on average, fully subject‐specific data predict muscle torques to a significantly greater degree of accuracy relative to generic data, although the extent of this is dependent on an individual subject's anthropometry, suggesting that in some cases, more generic muscle architecture data may be suitable for predicting complex muscle functions in musculoskeletal models if the methods for obtaining individualised data are not available.