Rapid Prediction of Personalised Muscle Mechanics: Integration with Diffusion Tensor Imaging
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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.
KeywordsFinite elements Triceps surae muscle mechanics Diffusion Tensor Imaging Partial Least Squares Regression
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