Optimal transport assisted full waveform inversion for multiparameter imaging of soft tissues in ultrasound computed tomography

Ultrasound computed tomography (USCT) has emerged as a promising platform for imaging tissue properties, offering non-ionizing and operator-independent capabilities. In this work, we demonstrate the feasibility of obtaining quantitative images of multiple acoustic parameters (sound speed and impedance) for soft tissues using full waveform inversion (FWI), which are justified with both numerical and experimental cases. A 3D reconstruction based on a series of 2D slice images is presented for the experimental case of ex vivo soft tissues. To improve the robustness of the reconstruction process, a hierarchical FWI strategy is adopted, gradually iterating from low to high frequencies. In parallel, we employ a graph-space optimal transport misfit function, avoiding convergence into local minima and minimizing inversion artifacts caused by skin-related supercritical reflections. Our method first carries out sound speed inversion based on transmitted waves in the low and middle frequency bands, and then uses all types of waves in the high frequency band for simultaneous inversion of both sound speed and impedance. Compared to conventional strategies, the proposed approach can accurately reconstruct physical models consistent with the actual soft tissue sample. These high-resolution ultrasound images of acoustic parameters are promising to allow for quantitative differentiation among different types of tissues (e.g., muscles and fats). These results have significant implications for advancing our understanding of tissue properties and for potentially contributing to disease diagnosis through USCT, which is a flexible and cost-effective alternative to X-ray computed tomography or magnetic resonance imaging at no significant sacrifices for resolution.