Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations

International Workshop on Point-of-Care Ultrasound

BIVPCS 2017, POCUS 2017: Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound pp 71–77Cite as

Rapid Prediction of Personalised Muscle Mechanics: Integration with Diffusion Tensor Imaging

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10549))

Abstract

Diffusion Tensor Imaging (DTI) has been widely used to characterise the 3D fibre architecture in both neural and muscle mechanics. However, the computational expense associated with continuum models make their use in graphics and medical visualisation intractable. This study presents an integration of continuum muscle mechanics with partial least squares regression to create a fast mechano-statistical model. We use the human triceps surae muscle as an example informed though DTI. Our statistical models predicted muscle shape (within 0.063 mm RMS error), musculotendon force (within 1% error), and tissue strain (within 8% max error during contraction). Importantly, the presented framework may play a role in addressing computational cost of predicting detailed muscle information through popular rigid body solvers such as OpenSIM.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Nielsen, P.M., et al.: Mathematical model of geometry and fibrous structure of the heart. Am. J. Physiol. 260(4 Pt. 2), H1365–H1378 (1991)

    Google Scholar 

  2. Hunter, P.J., et al.: Modeling the mechanical properties of cardiac muscle. Prog. Biophys. Mol. Biol. 69(2–3), 289–331 (1998)

    Article  Google Scholar 

  3. Hunter, P.J., et al.: An anatomical heart model with applications to myocardial activation and ventricular mechanics. Crit. Rev. Biomed. Eng. 20(5–6), 403–426 (1992)

    Google Scholar 

  4. Lemos, R.R., et al.: A framework for structured modeling of skeletal muscle. Comput. Methods Biomech. Biomed. Eng. 7(6), 305–317 (2004)

    Article  Google Scholar 

  5. Agur, A.M., et al.: Documentation and three-dimensional modelling of human soleus muscle architecture. Clin. Anat. 16(4), 285–293 (2003)

    Article  Google Scholar 

  6. Lin, Y.C., et al.: Two-dimensional surrogate contact modeling for computationally efficient dynamic simulation of total knee replacements. J. Biomech. Eng. 131(4), 041010 (2009)

    Article  Google Scholar 

  7. Zhang, J., et al.: Predictive statistical models of baseline variations in 3-D femoral cortex morphology. Med. Eng. Phys. 38(5), 450–457 (2016)

    Article  Google Scholar 

  8. Zhang, J., et al.: Lower limb estimation from sparse landmarks using an articulated shape model. J. Biomech. 49(16), 3875–3881 (2016)

    Article  Google Scholar 

  9. Stejskal, E.O., et al.: Spin diffusion measurements: spin echoes in the presence of time-dependent field gradient. J. Chem. Phys. 42(1), 288–292 (1965)

    Article  Google Scholar 

  10. Fernandez, J.W., et al.: Anatomically based geometric modelling of the musculo-skeletal system and other organs. Biomech. Model. Mechanobiol. 2(3), 139–155 (2004)

    Article  Google Scholar 

  11. Hunter, P., et al.: Integration from proteins to organs: the IUPS Physiome Project. Mech. Ageing Dev. 126(1), 187–192 (2005)

    Article  Google Scholar 

  12. Fernandez, J.W., et al.: An anatomically based patient-specific finite element model of patella articulation: towards a diagnostic tool. Biomech. Model. Mechanobiol. 4(1), 20–38 (2005)

    Article  Google Scholar 

  13. Kim, H.J., et al.: Evaluation of predicted knee-joint muscle forces during gait using an instrumented knee implant. J. Orthop. Res. 27(10), 1326–1331 (2009)

    Article  Google Scholar 

  14. Mateos-Aparicio, G.: Partial Least Squares (PLS) methods: origins, evolution, and application to social sciences. Commun. Stat. Theory Methods 40(13), 2305–2317 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  15. Handsfield, G.G., et al.: Determining skeletal muscle architecture with Laplacian simulations: a comparison with diffusion tensor imaging. Biomech. Model Mechanobiol. (2 June 2017). doi:10.1007/s10237-017-0923-5

Download references

Author information

Authors and Affiliations

  1. Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand

    J. Fernandez, K. Mithraratne, M. Alipour, G. Handsfield, T. Besier & J. Zhang

  2. Department of Engineering Science, University of Auckland, Auckland, New Zealand

    J. Fernandez & T. Besier

Authors
  1. J. Fernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Mithraratne
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Alipour
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Handsfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T. Besier
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Fernandez .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. Universidade do Porto, Porto, Portugal

    João Manuel R.S. Tavares

  4. Kitware Inc., Carrboro, North Carolina, USA

    Stephen Aylward

  5. University of Western Ontario, London, Ontario, Canada

    Shuo Li

  6. Johns Hopkins University, Baltimore, Maryland, USA

    Emad Boctor

  7. Queen’s University, Kingston, Ontario, Canada

    Gabor Fichtinger

  8. Children’s National Medical Center, Washington, District of Columbia, USA

    Kevin Cleary

  9. Washington University Medical Center, St. Louis, Missouri, USA

    Bradley Freeman

  10. InnerOptic Technology, Hillsborough, North Carolina, USA

    Luv Kohli

  11. Washington University Medical Center, St. Louis, Missouri, USA

    Deborah Shipley Kane

  12. Children’s National Medical Center, Washington, District of Columbia, USA

    Matt Oetgen

  13. Brigham and Women’s Hospital, Boston, Massachusetts, USA

    Sonja Pujol

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Fernandez, J., Mithraratne, K., Alipour, M., Handsfield, G., Besier, T., Zhang, J. (2017). Rapid Prediction of Personalised Muscle Mechanics: Integration with Diffusion Tensor Imaging. In: Cardoso, M., et al. Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound. BIVPCS POCUS 2017 2017. Lecture Notes in Computer Science(), vol 10549. Springer, Cham. https://doi.org/10.1007/978-3-319-67552-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67552-7_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67551-0

  • Online ISBN: 978-3-319-67552-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation