Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography

Ines E. Ulrich; Andrea Zunino; Christian Boehm; Andreas Fichtner

doi:10.1117/12.2580926

15 February 2021 Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography

Ines E. Ulrich, Andrea Zunino, Christian Boehm, Andreas Fichtner

Author Affiliations +

Proceedings Volume 11602, Medical Imaging 2021: Ultrasonic Imaging and Tomography; 116020Y (2021) https://doi.org/10.1117/12.2580926
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Ultrasound computed tomography (USCT) is a promising imaging modality for breast cancer screening. Two challenges commonly arising in time-of-flight USCT are (1) to efficiently deal with large data sets and (2) to effectively mitigate the ill-posedness for an adequate reconstruction of the model. In this contribution, we develop 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. The algorithms are tested on numerical as well as real data obtained from synthetic phantom scans of the previous USCT Data Challenges.

Conference Presentation

Citation Download Citation

Ines E. Ulrich, Andrea Zunino, Christian Boehm, and Andreas Fichtner "Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography", Proc. SPIE 11602, Medical Imaging 2021: Ultrasonic Imaging and Tomography, 116020Y (15 February 2021); https://doi.org/10.1117/12.2580926

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