Date of Award

2025

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Materials Science and Engineering (PhD)

Administrative Home Department

Department of Materials Science and Engineering

Advisor 1

Yongmei Jin

Advisor 2

Jingfeng Jiang

Committee Member 1

Stephen Kampe

Committee Member 2

Gowtham

Abstract

Ultrasound shear wave elastography (USWE) is an evolving and promising clinical tool for noninvasively measuring in vivo soft tissue biomechanical properties. Assumptions incorporated into the clinical workflow and technical limitations have created gaps between theoretical and clinically derived solutions. The heterogeneity of the fibrotic liver tissue, composition of the background, such as the presence of fatty liver tissue, and the preferred local orientation of the scarred fibrotic liver tissues embedded into the liver parenchyma, may contribute to the uncertainty in USWE measurements. This study aims to systematically investigate four cofounding factors (i.e., size, volume fraction, orientation of the fibrotic inclusions, fatty background) using computer simulations, to describe the multifaceted impact on shear wave speed (SWS) variability (i.e., SWS standard deviation [STD]). Additionally, a machine-learning-based approach will be used to establish a relationship between the simulated fibrotic liver tissue microstructure (i.e., spatial characteristics [SC]) and the calculated SWS variability.

Even though volume fraction and the SWS estimates (mean SWS and SWS-STD) were highly correlated, percent inclusion as a single predictive factor was not an accurate indicator of the SWS estimates. For this study, none of the individual SC features were able to predict the SWS estimates. Both SWS estimates provided unique information regarding the tissue model microstructure and should be considered when analyzing fibrotic liver tissue. Additionally, this study demonstrated that both fibrosis and fatty background impact the SWS-STD and that the SWS-STD distributions do not follow a Gaussian distribution. While the SWS-STD increased with fibrosis size and volume fraction, the SWS-STD decreased as the fatty background increased. The shape of the distribution did not follow a consistent trend across fibrosis inclusion levels, fibrosis sizes, fibrosis orientations, and percent fatty background for either SWS estimates. This study provided evidence that the current clinical guidelines, regarding cut-off values for the different METAVIR Fibrosis stages, overestimate the fibrosis levels in the presence of steatosis (i.e., fatty liver tissue). These findings demonstrate that USWE numerical modeling is a promising area for research that holds the potential to close the gaps between the 'true' biological tissue response and the clinically measured findings.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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