Quantitative inverse problem in visco-acoustic media under attenuation model uncertainty

TL;DR

This paper develops a robust iterative inversion algorithm for visco-acoustic media that accounts for model uncertainty and multiple reflections, demonstrated through ultrasound imaging simulations of breast tissue.

Contribution

It introduces a new inversion approach that handles attenuation model uncertainty and complex boundary reflections, improving robustness and scalability.

Findings

Algorithm is robust against attenuation model uncertainty.

Complex frequency inversion mitigates multiple reflection issues.

Successful large-scale 3D ultrasound imaging simulations.

Abstract

We consider the inverse problem of quantitative reconstruction of properties (e.g., bulk modulus, density) of visco-acoustic materials based on measurements of responding waves after stimulation of the medium. Numerical reconstruction is performed by an iterative minimization algorithm. Firstly, we investigate the robustness of the algorithm with respect to attenuation model uncertainty, that is, when different attenuation models are used to simulate synthetic observation data and for the inversion, respectively. Secondly, to handle data-sets with multiple reflections generated by wall boundaries around the domain, we perform inversion using complex frequencies, and show that it offers a robust framework that alleviates the difficulties of multiple reflections. To illustrate the efficiency of the algorithm, we perform numerical simulations of ultrasound imaging experiments to reconstruct a synthetic breast sample that contains an inclusion of high-contrast properties. We perform experiments in two and three dimensions, where the latter also serves to demonstrate the numerical feasibility in a large-scale configuration.