PhD Defence notice: "Robust Sonographic Muscle Quality Assessment: A Real-Time, Accurate Speed-of-Sound Estimation Framework"

Candidate: Di Xiao
Title: Robust Sonographic Muscle Quality Assessment: A Real-Time, Accurate Speed-of-Sound Estimation Framework
Date: February 2, 2024
Time: 1:30 PM
Place: EIT 3142
Supervisor(s): Yu, Alfred

Abstract:
Muscle quality can act as an indicator for physical health through qualitative – yet measurable – changes in muscle architecture and composition. One option for assessing muscle quality is through musculoskeletal ultrasound, which can provide metrics such as echogenicity or texture. These existing ultrasound-based metrics relating to muscle quality can depend on scanner capabilities and operator assessment of the resulting B-mode images. Tissue speed-of-sound (SoS) can robustly augment muscle quality assessment as an intrinsic property of the tissue; however, sonographic measurement of muscle speed-of-sound is frequently limited by hardware which requires bilateral access to the tissue or which cannot perform simultaneous ultrasound imaging in real-time.

In this dissertation, I designed an ultrasound framework for live, accurate SoS estimation in vivo. A novel global SoS estimation algorithm with real-time potential was created using the principles of high-frame-rate ultrasound with a standard pulse-echo ultrasound probe. This algorithm was experimentally validated to be highly accurate both in vitro and in vivo with demonstrable live imaging capacity; a portable research scanner and laptop were used to realize the framework. In the process of framework design, the underlying engineering challenges of real-time data transfer rates from the probe to the system and image formation efficiency were addressed using deep learning principles and sparse matrix formulations respectively. Lastly, the novel framework was then used to conduct a human study consisting of forty volunteers. The study served to demonstrate the applicability of a live SoS system, establish a replicable experimental protocol for SoS measurement in large muscles, and investigate relationships between demographics and muscle SoS.

This dissertation research is intended to bridge the engineering innovations of ultrasound algorithm development with the clinical applications for SoS as a tissue biomarker. For the targeted muscle quality application, the results confirm the physiological relevance of muscle SoS and exhibit the utility of real time SoS estimation with simultaneous B-mode imaging. The significant relationships between muscle SoS and demographic factors support the potential for clinical translation of such a real-time system. The realization of live SoS estimation can help derive new insights between the tissue SoS and pathological conditions or imaging applications.