Detecting muscle activation using ultrasound speed of sound inversion with deep learning

TL;DR

This paper explores using deep learning to invert ultrasound sound speed data for dynamic muscle imaging, enabling detection of muscle activation over large areas at high frame rates.

Contribution

It introduces a novel deep learning approach for sound speed inversion from ultrasound data to visualize active muscles during movement.

Findings

Dynamic muscle contraction detected in calf muscles.

Deep learning-based sound speed inversion successfully maps active muscles.

High frame rates are necessary for real-time dynamic muscle imaging.

Abstract

Functional muscle imaging is essential for diagnostics of a multitude of musculoskeletal afflictions such as degenerative muscle diseases, muscle injuries, muscle atrophy, and neurological related issues such as spasticity. However, there is currently no solution, imaging or otherwise, capable of providing a map of active muscles over a large field of view in dynamic scenarios. In this work, we look at the feasibility of longitudinal sound speed measurements to the task of dynamic muscle imaging of contraction or activation. We perform the assessment using a deep learning network applied to pre-beamformed ultrasound channel data for sound speed inversion. Preliminary results show that dynamic muscle contraction can be detected in the calf and that this contraction can be positively assigned to the operating muscles. Potential frame rates in the hundreds to thousands of frames per second are necessary to accomplish this.