Abstract: Ultrasound Computed Tomography (USCT), also known as Full Waveform Inversion (FWI), reconstructs the mechanical properties of biological tissues by modeling the full propagation of ultrasound waves. This modality shows great promise for advanced applications such as breast, neuro, and prostate imaging, yet its clinical adoption has been limited by the trade-off between accuracy and computational efficiency. Physics-based reconstruction methods achieve high-resolution, quantitative maps of tissue properties but are computationally demanding and sensitive to model uncertainties. Data-driven approaches, particularly deep learning, have recently offered accelerated solutions but often lack robustness and generalizability. In this work, we present hybrid USCT strategies that bridge wave physics and machine learning. By embedding physical principles into self-supervised learning frameworks, our methods substantially reduce computational cost while maintaining reconstruction fidelity. We demonstrate their efficacy in challenging prostate imaging scenarios, highlighting their potential to advance USCT toward real-time clinical translation.

Short Bio: Youzuo Lin is an Associate Professor in the School of Data Science and Society at the University of North Carolina at Chapel Hill. Previously, he served as a Senior Scientist at Los Alamos National Laboratory. He earned his Ph.D. in Applied and Computational Mathematics from Arizona State University in 2010. Youzuo’s research focuses on scientific machine learning methods and their applications, particularly in computational wave imaging, ultrasound tomography, geophysical inversion, and UAV image analysis. He has published over 100 articles in leading journals and conference proceedings and is a co-inventor on several U.S. patents related to ultrasound imaging techniques.