In vivo human lower limb muscle architecture dataset obtained using diffusion tensor imaging.

Charles, James P ORCID: 0000-0001-8256-8035, Suntaxi, Felipe and Anderst, William J
(2019) In vivo human lower limb muscle architecture dataset obtained using diffusion tensor imaging. PloS one, 14 (10). e0223531-e0223531.

[thumbnail of Charles et al 2019.pdf] Text
Charles et al 2019.pdf - Published version

Download (1MB) | Preview


'Gold standard' reference sets of human muscle architecture are based on elderly cadaveric specimens, which are unlikely to be representative of a large proportion of the human population. This is important for musculoskeletal modeling, where the muscle force-generating properties of generic models are defined by these data but may not be valid when applied to models of young, healthy individuals. Obtaining individualized muscle architecture data in vivo is difficult, however diffusion tensor magnetic resonance imaging (DTI) has recently emerged as a valid method of achieving this. DTI was used here to provide an architecture data set of 20 lower limb muscles from 10 healthy adults, including muscle fiber lengths, which are important inputs for Hill-type muscle models commonly used in musculoskeletal modeling. Maximum isometric force and muscle fiber lengths were found not to scale with subject anthropometry, suggesting that these factors may be difficult to predict using scaling or optimization algorithms. These data also highlight the high level of anatomical variation that exists between individuals in terms of lower limb muscle architecture, which supports the need of incorporating subject-specific force-generating properties into musculoskeletal models to optimize their accuracy for clinical evaluation.

Item Type: Article
Uncontrolled Keywords: Lower Extremity, Muscle, Skeletal, Humans, Organ Size, Adult, Female, Male, Databases as Topic, Diffusion Tensor Imaging
Depositing User: Symplectic Admin
Date Deposited: 25 Oct 2019 09:06
Last Modified: 19 Jan 2023 00:21
DOI: 10.1371/journal.pone.0223531
Related URLs: