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Proceedings Paper

NETT regularization for compressed sensing photoacoustic tomography
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Paper 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.

Paper Details

Date Published: 27 February 2019
PDF: 11 pages
Proc. SPIE 10878, Photons Plus Ultrasound: Imaging and Sensing 2019, 108783B (27 February 2019); doi: 10.1117/12.2508486
Show Author Affiliations
Stephan Antholzer, Univ. Innsbruck (Austria)
Johannes Schwab, Univ. Innsbruck (Austria)
Johnnes Bauer-Marschallinger, Research Ctr. for Non-Destructive Testing (RECENDT) (Austria)
Peter Burgholzer, Research Ctr. for Non-Destructive Testing (RECENDT) (Austria)
Markus Haltmeier, Univ. Innsbruck (Austria)


Published in SPIE Proceedings Vol. 10878:
Photons Plus Ultrasound: Imaging and Sensing 2019
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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