Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography

Abstract

Ultrasound computed tomography (USCT) is a promising imaging modality for breast cancer screening. Three challenges commonly arising in time-of-flight USCT are (1) to choose a physical forward model that describes acoustic wave propagation in an inhomogeneous medium appropriately (2) to effectively mitigate the ill-posedness for an adequate reconstruction of the model and (3) to efficiently deal with large data sets. In this contribution, we investigate methods that address these three challenges by developing an optimization strategy based on a stochastic descent method that adaptively subsamples the data and analyze its performance in combination with different sparsity-enforcing regularization techniques.