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.

Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.


Lieber, R. L. , and Fridén, J. , 2000, “ Functional and Clinical Significance of Skeletal Muscle Architecture,” Muscle Nerve, 23(11), pp. 1647–66. [CrossRef] [PubMed]
Delp, S. L. , Loan, J. P. , Hoy, M. G. , Zajac, F. E. , Topp, E. L. , and Rosen, J. M. , 1990, “ An Interactive Graphics-Based Model of the Lower Extremity to Study Orthopaedic Surgical Procedures,” IEEE Trans Biomed Eng, 37(8), pp. 757–67. [CrossRef] [PubMed]
Delp, S. L. , and Loan, J. P. , 1995, “ A Graphics-Based Software System to Develop and Analyze Models of Musculoskeletal Structures,” Comput. Biol. Med., 25(1), pp. 21–34. [CrossRef] [PubMed]
Hutchinson, J. R. , Anderson, F. C. , Blemker, S. S. , and Delp, S. L. , 2005, “ Analysis of Hindlimb Muscle Moment Arms in Tyrannosaurus Rex Using a Three-Dimensional Musculoskeletal Computer Model: Implications for Stance, Gait, and Speed,” Paleobiology, 31(4), pp. 676–701. [CrossRef]
Delp, S. L. , Anderson, F. C. , Arnold, A. S. , Loan, P. , Habib, A. , John, C. T. , Guendelman, E. , and Thelen, D. G. , 2007, “ OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement,” IEEE Trans Biomed Eng, 54(11), pp. 1940–1950. [CrossRef] [PubMed]
Arnold, E. M. , Ward, S. R. , Lieber, R. L. , and Delp, S. L. , 2010, “ A Model of the Lower Limb for Analysis of Human Movement,” Ann. Biomed. Eng., 38(2), pp. 269–279. [CrossRef] [PubMed]
O'Neill, M. C. , Lee, L. F. , Larson, S. G. , Demes, B. , Stern, J. T. , and Umberger, B. R. , 2013, “ A Three-Dimensional Musculoskeletal Model of the Chimpanzee (Pan troglodytes) Pelvis and Hind Limb,” J. Exp. Biol., 216(19), pp. 3709–3723. [CrossRef] [PubMed]
Rankin, J , Rubenson, J , and Hutchinson, J. R. , 2016, “ Inferring Muscle Functional Roles of the Ostrich Pelvic Limb During Walking and Running Using Computer Optimization,” J. R. Soc. Interface, 13(118).
Charles, J. P. , Cappellari, O. , Spence, A. J. , Wells, D. J. , and Hutchinson, J. R. , 2016, “ Muscle Moment Arms and Sensitivity Analysis of a Mouse Hindlimb Musculoskeletal Model,” J. Anat., 229(4), pp. 514–535. [CrossRef] [PubMed]
Handsfield, G. G. , Meyer, C. H. , Hart, J. M. , Abel, M. F. , and Blemker, S. S. , 2014, “ Relationships of 35 Lower Limb Muscles to Height and Body Mass Quantified Using MRI,” J. Biomech., 47(3), pp. 631–638. [CrossRef] [PubMed]
Gerus, P. , Rao, G. , and Berton, E. , 2012, “ Subject-Specific Tendon-Aponeurosis Definition in Hill-Type Model Predicts Higher Muscle Forces in Dynamic Tasks,” PLoS One, 7(8), p. e44406. [CrossRef] [PubMed]
de Oliveira, L. F. , and Menegaldo, L. L. , 2010, “ Individual-Specific Muscle Maximum Force Estimation Using Ultrasound for Ankle Joint Torque Prediction Using an EMG-Driven Hill-Type Model,” J. Biomech., 43(14), pp. 2816–21. [CrossRef] [PubMed]
Scovil, C. Y. , and Ronsky, J. L. , 2006, “ Sensitivity of a Hill-Based Muscle Model to Perturbations in Model Parameters,” J. Biomech., 39(11), pp. 2055–2063. [CrossRef] [PubMed]
Ackland, D. C. , Lin, Y. C. , and Pandy, M. G. , 2012, “ Sensitivity of Model Predictions of Muscle Function to Changes in Moment Arms and Muscle-Tendon Properties: A Monte Carlo Analysis,” J. Biomech., 45(8), pp. 1463–1471. [CrossRef] [PubMed]
Valente, G. , Pitto, L. , Testi, D. , Seth, A. , Delp, S. L. , Stagni, R. , Viceconti, M. , and Taddei, F. , 2014, “ Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?,” PLoS One, 9(11), p. e112625. [CrossRef] [PubMed]
Navacchia, A. , Myers, C. A. , Rullkoetter, P. J. , and Shelburne, K. B. , 2016, “ Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis,” ASME J. Biomech. Eng., 138(3), p. 4032379. [CrossRef]
Arnold, A. S. , Salinas, S. , Asakawa, D. J. , and Delp, S. L. , 2000, “ Accuracy of Muscle Moment Arms Estimated From MRI-Based Musculoskeletal Models of the Lower Extremity,” Comput. Aided Surg., 5(2), pp. 108–119. [CrossRef] [PubMed]
Modenese, L. , Ceseracciu, E. , Reggiani, M. , and Lloyd, D. G. , 2016, “ Estimation of Musculotendon Parameters for Scaled and Subject Specific Musculoskeletal Models Using an Optimization Technique,” J. Biomech., 49(2), pp. 141–148. [CrossRef] [PubMed]
Wu, W. , Lee, P. V. , Bryant, A. L. , Galea, M. , and Ackland, D. C. , 2016, “ Subject-Specific Musculoskeletal Modeling in the Evaluation of Shoulder Muscle and Joint Function,” J. Biomech., 49(15), pp. 3626–3634. [CrossRef] [PubMed]
Bolsterlee, B. , Veeger, H. E. , van der Helm, F. C. , Gandevia, S. C. , and Herbert, R. D. , 2015, “ Comparison of Measurements of Medial Gastrocnemius Architectural Parameters From Ultrasound and Diffusion Tensor Images,” J. Biomech., 48(6), pp. 1133–1140. [CrossRef] [PubMed]
Lee, D. , Li, Z. , Sohail, Q. Z. , Jackson, K. , Fiume, E. , and Agur, A. , 2015, “ A Three-Dimensional Approach to Pennation Angle Estimation for Human Skeletal Muscle,” Comput. Methods Biomech. Biomed. Eng., 18(13), pp. 1474–1484. [CrossRef]
Assaf, Y. , and Pasternak, O. , 2008, “ Diffusion Tensor Imaging (DTI)-Based White Matter Mapping in Brain Research: A Review,” J. Mol. Neurosci., 34(1), pp. 51–61. [CrossRef] [PubMed]
Chen, L. , Hu, X. , Ouyang, L. , He, N. , Liao, Y. , Liu, Q. , Zhou, M. , Wu, M. , Huang, X. , and Gong, Q. , 2016, “ A Systematic Review and Meta-Analysis of Tract-Based Spatial Statistics Studies Regarding Attention-Deficit/Hyperactivity Disorder,” Neurosci. Biobehav. Rev., 68, pp. 838–847. [CrossRef] [PubMed]
Eierud, C. , Craddock, R. C. , Fletcher, S. , Aulakh, M. , King-Casas, B. , Kuehl, D. , and LaConte, S. M. , 2014, “ Neuroimaging After Mild Traumatic Brain Injury: Review and Meta-Analysis,” Neuroimage Clin., 4, pp. 283–94. [CrossRef] [PubMed]
Soares, J. M. , Marques, P. , Alves, V. , and Sousa, N. , 2013, “ A Hitchhiker's Guide to Diffusion Tensor Imaging,” Front. Neurosci., 7, p. 31. [CrossRef] [PubMed]
Seif, M. , Lu, H. , Boesch, C. , Reyes, M. , and Vermathen, P. , 2015, “ Image Registration for Triggered and Non-Triggered DTI of the Human Kidney: Reduced Variability of Diffusion Parameter Estimation,” J. Magn. Reson. Imaging, 41(5), pp. 1228–1235. [CrossRef] [PubMed]
Hsu, E. W. , Buckley, D. L. , Bui, J. D. , Blackband, S. J. , and Forder, J. R. , 2001, “ Two-Component Diffusion Tensor MRI of Isolated Perfused Hearts,” Magn. Reson. Med., 45 (6), pp. 1039–1045. [CrossRef] [PubMed]
Hsu, E. W. , and Mori, S. , 1995, “ Analytical Expressions for the NMR Apparent Diffusion Coefficients in an Anisotropic System and a Simplified Method for Determining Fiber Orientation,” Magn. Reson. Med., 34(2), pp. 194–200. [CrossRef] [PubMed]
Damon, B. M. , Ding, Z. , Anderson, A. W. , Freyer, A. S. , and Gore, J. C. , 2002, “ Validation of Diffusion Tensor MRI-Based Muscle Fiber Tracking,” Magn. Reson. Med., 48(1), pp. 97–104. [CrossRef] [PubMed]
Deux, J. F. , Malzy, P. , Paragios, N. , Bassez, G. , Luciani, A. , Zerbib, P. , Roudot-Thoraval, F. , Vignaud, A. , Kobeiter, H. , and Rahmouni, A. , 2008, “ Assessment of Calf Muscle Contraction by Diffusion Tensor Imaging,” Eur. Radiol., 18(10), pp. 2303–2310. [CrossRef] [PubMed]
Heemskerk, A. M. , Sinha, T. K. , Wilson, K. J. , Ding, Z. , and Damon, B. M. , 2010, “ Repeatability of DTI-Based Skeletal Muscle Fiber Tracking,” NMR Biomed., 23(3), pp. 294–303. [PubMed]
Sinha, U. , Sinha, S. , Hodgson, J. A. , and Edgerton, R. V. , 2011, “ Human Soleus Muscle Architecture at Different Ankle Joint Angles From Magnetic Resonance Diffusion Tensor Imaging,” J. Appl. Physiol., 110(3), pp. 807–819. [CrossRef] [PubMed]
Froeling, M. , Nederveen, A. J. , Heijtel, D. F. , Lataster, A. , Bos, C. , Nicolay, K. , Maas, M. , Drost, M. R. , and Strijkers, G. J. , 2012, “ Diffusion-Tensor MRI Reveals the Complex Muscle Architecture of the Human Forearm,” J. Magn. Reson. Imaging, 36(1), pp. 237–248. [CrossRef] [PubMed]
Froeling, M. , Oudeman, J. , Strijkers, G. J. , Maas, M. , Drost, M. R. , Nicolay, K. , and Nederveen, A. J. , 2015, “ Muscle Changes Detected With Diffusion-Tensor Imaging After Long-Distance Running,” Radiology, 274(2), pp. 548–62. [CrossRef] [PubMed]
Damon, B. M. , Froeling, M. , Buck, A. K. , Oudeman, J. , Ding, Z. , Nederveen, A. J. , Bush, E. C. , and Strijkers, G. J. , 2017, “ Skeletal Muscle Diffusion Tensor-MRI Fiber Tracking: Rationale, Data Acquisition and Analysis Methods, Applications and Future Directions,” NMR Biomed, 30(3), p. e3563.
Sieben, J. M. , van Otten, I. , Lataster, A. , Froeling, M. , Nederveen, A. J. , Strijkers, G. J. , and Drost, M. R. , 2016, “ In Vivo Reconstruction of Lumbar Erector Spinae Architecture Using Diffusion Tensor MRI,” Clin. Spine Surg., 29(3), pp. E139–E145. [CrossRef] [PubMed]
Bolsterlee, B. , Finni, T. , D'Souza, A. , Eguchi, J. , Clarke, E. C. , and Herbert, R. D. , 2018, “ Three-Dimensional Architecture of the Whole Human Soleus Muscle In Vivo,” PeerJ, 6, p. e4610. [CrossRef] [PubMed]
Mendez, J. , and Keys, A. , 1960, “ Density and Composition of Mammalian Skeletal Muscle,” Metabolism, 9(2), pp. 184–188. https://eurekamag.com/research/024/450/024450136.php
Wickiewicz, T. L. , Roy, R. R. , Powell, P. L. , and Edgerton, V. R. , 1983, “ Muscle Architecture of the Human Lower Limb,” Clin. Orthop. Relat. Res., 179(1), pp. 275–283. [CrossRef]
Ward, S. R. , Eng, C. M. , Smallwood, L. H. , and Lieber, R. L. , 2009, “ Are Current Measurements of Lower Extremity Muscle Architecture Accurate?,” Clin. Orthop. Relat. Res., 467(4), pp. 1074–1082. [CrossRef] [PubMed]
Ward, S. R. , and Lieber, R. L. , 2005, “ Density and Hydration of Fresh and Fixed Human Skeletal Muscle,” J. Biomech., 38(11), pp. 2317–2320. [CrossRef] [PubMed]
Jiang, H. , van Zijl, P. C. , Kim, J. , Pearlson, G. D. , and Mori, S. , 2006, “ DtiStudio: Resource Program for Diffusion Tensor Computation and Fiber Bundle Tracking,” Comput Methods Programs Biomed, 81(2), pp. 106–116. [CrossRef] [PubMed]
Aja-Fernandez, S. , Niethammer, M. , Kubicki, M. , Shenton, M. E. , and Westin, C. F. , 2008, “ Restoration of DWI Data Using a Rician LMMSE Estimator,” IEEE Trans. Med. Imaging, 27(10), pp. 1389–1403. [CrossRef] [PubMed]
Cook, P. A. , Bai, Y. , Nedjati-Gilani, S. , Seunarine, K. K. , Hall, M. G. , Parker, G. J. , and Alexander, D. C. , 2006, “ Camino: Open-Source Diffusion-MRI Reconstruction and Processing,” 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Seattle, WA, May 6–12, p. p2759.
Yushkevich, P. A. , Piven, J. , Hazlett, H. C. , Smith, R. G. , Ho, S. , Gee, J. C. , and Gerig, G. , 2006, “ User-Guided 3D Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability,” Neuroimage, 31(3), pp. 1116–1128. [CrossRef] [PubMed]
Schneider, C. A. , Rasband, W. S. , and Eliceiri, K. W. , 2012, “ NIH Image to ImageJ: 25 Years of Image Analysis,” Nat. Methods, 9(7), pp. 671–675. [CrossRef] [PubMed]
Raiteri, B. J. , Cresswell, A. G. , and Lichtwark, G. A. , 2016, “ Three-Dimensional Geometrical Changes of the Human Tibialis Anterior Muscle and Its Central Aponeurosis Measured With Three-Dimensional Ultrasound During Isometric Contractions,” PeerJ, 4, p. e2260. [CrossRef] [PubMed]
Zajac, F. E. , 1989, “ Muscle and Tendon: Properties, Models, Scaling, and Application to Biomechanics and Motor Control,” Crit. Rev. Biomed. Eng., 17(4), pp. 359–411. http://e.guigon.free.fr/rsc/article/Zajac89.pdf [PubMed]
Narici, M. V. , Maganaris, C. N. , Reeves, N. D. , and Capodaglio, P. , 2003, “ Effect of Aging on Human Muscle Architecture,” J. Appl. Physiol., 95(6), pp. 2229–2234. [CrossRef] [PubMed]
Cutts, A. , 1988, “ The Range of Sarcomere Lengths in the Muscles of the Human Lower Limb,” J. Anat., 160, pp. 79–88. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1262051/pdf/janat00172-0084.pdf [PubMed]
Chen, X. , and Delp, S. L. , 2016, “ Human Soleus Sarcomere Lengths Measured Using In Vivo Microendoscopy at Two Ankle Flexion Angles,” J. Biomech., 49(16), pp. 4164–4167. [CrossRef] [PubMed]
Chen, X. , Sanchez, G. N. , Schnitzer, M. J. , and Delp, S. L. , 2016, “ Changes in Sarcomere Lengths of the Human Vastus Lateralis Muscle With Knee Flexion Measured Using In Vivo Microendoscopy,” J. Biomech., 49(13), pp. 2989–2994. [CrossRef] [PubMed]
Yanagisawa, O. , Kurihara, T. , Kobayashi, N. , and Fukubayashi, T. , 2011, “ Strenuous Resistance Exercise Effects on Magnetic Resonance Diffusion Parameters and Muscle-Tendon Function in Human Skeletal Muscle,” J. Magn. Reson. Imaging, 34(4), pp. 887–894. [CrossRef] [PubMed]


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.



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In