Detecting muscle activation using ultrasound speed of sound inversion with deep learning
This addresses the lack of imaging solutions for mapping active muscles in dynamic scenarios, which is crucial for diagnosing musculoskeletal and neurological issues, but it is incremental as it builds on existing ultrasound and deep learning methods.
The paper tackled the problem of dynamic muscle imaging for diagnostics by using deep learning on ultrasound channel data to invert sound speed measurements, achieving detection of muscle contraction in the calf with potential frame rates of hundreds to thousands per second.
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.