Code from: In vivo human lower limb muscle architecture dataset obtained using diffusion tensor imaging
Internal Dataset

UID: 79

Author(s): Charles, James P.*, Suntaxi, Felipe+, Gale, Tom+, Anderst, William+ * Corresponding Author + University of Pittsburgh Author

This MATLAB script finds mean and median fiber (muscle fascicle) length of a given muscle obj file generated from DTI (diffusion tensor magnetic resonance imaging) fiber tractography. In the study which produced this code, the authors used DTI to obtain individualized muscle architecture data in vivo for 10 young, healthy adult adult volunteers. Full data processing methods, including pre-processing, signal-to-noise ratio improvement, tensor estimation, and fiber tract estimation and smoothing, are described in the PLOS citation, which includes tabular data.

From the article abstract:

"‘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."

Subject of Study
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Download MATLAB code via Figshare. Data used with this code are available in table form in the accompanying paper.
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Dataset Format(s)
MATLAB (.m, .mat)
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3.51 kB
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