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Special Section: Spotlight on the Future–Imaging and Biomechanical Engineering

Determining Subject-Specific Lower-Limb Muscle Architecture Data for Musculoskeletal Models Using Diffusion Tensor Imaging

[+] Author and Article Information
James P. Charles

Biodynamics Lab,
Department of Orthopaedic Surgery,
University of Pittsburgh,
Pittsburgh, PA 15203
e-mail: J.Charles@liverpool.ac.uk

Chan-Hong Moon

Magnetic Resonance Research Center,
Department of Radiology,
University of Pittsburgh,
Pittsburgh, PA 15213

William J. Anderst

Biodynamics Lab,
Department of Orthopaedic Surgery,
University of Pittsburgh,
Pittsburgh, PA 15203

1Corresponding author.

2Present address: Department of Musculoskeletal Biology Institute of Aging and Chronic Disease University of Liverpool, Liverpool, UK.

Manuscript received February 21, 2018; final manuscript received July 3, 2018; published online April 22, 2019. Assoc. Editor: Joel D. Stitzel.

J Biomech Eng 141(6), 060905 (Apr 22, 2019) (9 pages) Paper No: BIO-18-1098; doi: 10.1115/1.4040946 History: Received February 21, 2018; Revised July 03, 2018

Accurate individualized muscle architecture data are crucial for generating subject-specific musculoskeletal models to investigate movement and dynamic muscle function. Diffusion tensor imaging (DTI) magnetic resonance (MR) imaging has emerged as a promising method of gathering muscle architecture data in vivo; however, its accuracy in estimating parameters such as muscle fiber lengths for creating subject-specific musculoskeletal models has not been tested. Here, we provide a validation of the method of using anatomical magnetic resonance imaging (MRI) and DTI to gather muscle architecture data in vivo by directly comparing those data obtained from MR scans of three human cadaveric lower limbs to those from dissections. DTI was used to measure fiber lengths and pennation angles, while the anatomical images were used to estimate muscle mass, which were used to calculate physiological cross-sectional area (PCSA). The same data were then obtained through dissections, where it was found that on average muscle masses and fiber lengths matched well between the two methods (4% and 1% differences, respectively), while PCSA values had slightly larger differences (6%). Overall, these results suggest that DTI is a promising technique to gather in vivo muscle architecture data, but further refinement and complementary imaging techniques may be needed to realize these goals.

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Figures

Grahic Jump Location
Fig. 1

Flowchart detailing the general overview of the methods in this study. Vm–muscle belly volume, Lf–fiber length, θ–fiber pennation angle, Mm–muscle belly mass, PCSA.

Grahic Jump Location
Fig. 2

T1 MR images of the thigh (a) and lower leg (b) segments of one of the cadaver specimens. 20 muscles from each cadaver, as well as bones, were digitally segmented to create 3D representations of the limbs ((c) anterior view and (d) posterior view). The volumetric meshes of each muscle were used to estimate mass. For muscle abbreviation definitions, see Table1.

Grahic Jump Location
Fig. 3

Diffusion-weighted images of the upper thigh through several stages of preprocessing before fiber tracking: (a) nonregistered image, (b) after automated image registration in DTIstudio, (c) after Rician noise reduction in MedINRIA, and (d) colored FA map, in relation to eigenvector 1 (λ1), and based on the diffusion tensors calculated in MedINRIA. Blue colors (or darker shades) indicate a superior-inferior direction of the muscle fibers, while green colors (or lighter shades) represent more anterior-posterior directions.

Grahic Jump Location
Fig. 4

The fibers of the Vastus medialis muscle (a), tracked from the diffusion weighted MR images. From these 3D point cloud based models, it was possible to measure fiber length (Lf) and fiber pennation angle (θ, angle of the fibers relative to the muscle's line of action (blue vertical line), as also shown in (a). A 3D volumetric mesh of the same muscle (b), created from digital segmentation of T1-weighted MR images, is shown for reference.

Grahic Jump Location
Fig. 5

The semi-membranosus muscle from a cadaver, showing the empirical determination of muscle fiber length (Lf; assumed here to be equal to muscle fascicle bundle length) and pennation angle (θ; angle of the fibers relative to the muscle's internal tendon, or line of action) during dissections

Grahic Jump Location
Fig. 6

Mean muscle masses (a) and fiber lengths (b) cross three cadaver specimens estimated from dissections and MRI sequences, compared to the previously published data from Ward et al. (2009). Error bars represent + 1 standard deviation. For muscle abbreviations, see Table 1.

Grahic Jump Location
Fig. 7

Mean PCSA values (a) and mean pennation angles (b) across three cadaver specimens, estimated from dissections and MR images, compared to the previously published data from Ward et al. [40]. Error bars represent + 1 standard deviation. For muscle abbreviations, see Table 1.

Grahic Jump Location
Fig. 8

Mean fiber length muscle length ratio (Lf:Lm) values across three cadaver specimens, estimated from dissections and MR images, compared to the previously published data from Ward et al. [40]. Error bars represent + 1 standard deviation. For muscle abbreviations, see Table 1.

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