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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10549)


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.


Finite elements Triceps surae muscle mechanics Diffusion Tensor Imaging Partial Least Squares Regression 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Auckland Bioengineering InstituteUniversity of AucklandAucklandNew Zealand
  2. 2.Department of Engineering ScienceUniversity of AucklandAucklandNew Zealand

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