Differentiating Ultrasound-Measured Active and Passive Muscle Deformation via Statistical Shape Modeling

Abstract

BACKGROUND AND AIMS: Muscle deformation — as measured via ultrasound — constitutes a promising signal from which to infer individual muscle forces, a key quantity in biomechanical analysis. Simple deformation measures (e.g. thickness, cross-sectional area) reflect both force output and passive soft tissue dynamics due to changes in configuration. In this work, we formulate statistical shape models (SSMs) to discriminate between active and passive sources of deformation within a single ultrasound cross section using the OpenArm 2.0 data set (Nozik/Hallock et al. 2019) of 3D ultrasound scans of the biceps brachii under factorial elbow angles and loading conditions.
METHODS: We first identified cross section locations in OpenArm 2.0 scans with the most consistent deformations, then used Procrustes analysis to remove non-shape-associated variation. Principal component analysis was applied separately to scans varying only in force or only in kinematics, and SSMs were built for each group to reveal shape features linked to active (force) and passive (kinematic) deformation.
RESULTS: Changes in thickness were prominent in the major principal components of both force- and kinematically-varying models, supporting the inadequacy of such simple measures alone. Considering multiple principal components, preliminary results suggest active deformation may best be quantified by changes in overall cross section size while passive deformation may best be quantified by measures of shape.
CONCLUSION: Preliminary results highlight the feasibility of SSM as a tool to differentiate between active and passive muscle deformation. We aim to expand model capabilities to account for both sources of deformation simultaneously, toward real-time models of muscle force output for more accurate neuromusculoskeletal simulations.

Text has been modified from initial submission to reflect improved analyses at poster presentation time.