NETT regularization for compressed sensing photoacoustic tomography

Stephan Antholzer; Johannes Schwab; Johnnes Bauer-Marschallinger; Peter Burgholzer; Markus Haltmeier

doi:10.1117/12.2508486

27 February 2019 NETT regularization for compressed sensing photoacoustic tomography

Stephan Antholzer, Johannes Schwab, Johnnes Bauer-Marschallinger, Peter Burgholzer, Markus Haltmeier

Proceedings Volume 10878, Photons Plus Ultrasound: Imaging and Sensing 2019; 108783B (2019) https://doi.org/10.1117/12.2508486
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

We discuss several methods for image reconstruction in compressed sensing photoacoustic tomography (CS-PAT). In particular, we apply the deep learning method of [H. Li, J. Schwab, S. Antholzer, and M. Haltmeier. NETT: Solving Inverse Problems with Deep Neural Networks (2018), arXiv:1803.00092], which is based on a learned regularizer, for the first time to the CS-PAT problem. We propose a network architecture and training strategy for the NETT that we expect to be useful for other inverse problems as well. All algorithms are compared and evaluated on simulated data, and validated using experimental data for two different types of phantoms. The results one the hand indicate great potential of deep learning methods, and on the other hand show that significant future work is required to improve their performance on real-word data.

Citation Download Citation

Stephan Antholzer, Johannes Schwab, Johnnes Bauer-Marschallinger, Peter Burgholzer, and Markus Haltmeier "NETT regularization for compressed sensing photoacoustic tomography", Proc. SPIE 10878, Photons Plus Ultrasound: Imaging and Sensing 2019, 108783B (27 February 2019); https://doi.org/10.1117/12.2508486

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