Towards rapid prediction of personalised muscle mechanics: integration with diffusion tensor imaging

Abstract

Diffusion tensor imaging (DTI) has been widely used to characterise the 3D fascicle architecture in 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 mechanostatistical model. We use the human gastrocnemius muscle (medial and lateral heads) as an example informed though DTI. Our statistical models predicted muscle shape (within 0.063 mm root-mean-square (RMS) error), musculotendon force (within 1% error), and tissue strain (within 8% max error during muscle contraction), compared to a finite-element model simulation. The technique presented here is a step towards integrating expensive continuum mechanics with fast rigid body solutions in biomechanics. While muscle force is a primary objective in rigid body solvers the additional information including 3D muscle shape and stress/strain fields may now be integrated. One of the key benefits is that muscle interaction with other soft tissues is accounted for, muscle moment arms are not estimated, and the detailed rich 3D continuum fascicle architecture is no longer simplified but plays a full role in biomechanics simulation. This has implications for musculoskeletal biomechanics, orthopaedics and medical visualisation.