NETT Regularization for Compressed Sensing Photoacoustic Tomography

TL;DR

This paper explores the application of NETT, a deep learning-based regularization method, to compressed sensing photoacoustic tomography, demonstrating promising results but also highlighting the need for further improvements on real-world data.

Contribution

It introduces a novel network architecture and training strategy for applying NETT to CS-PAT, extending deep learning regularization techniques to this imaging modality.

Findings

Deep learning methods show great potential for CS-PAT reconstruction.

The proposed NETT approach performs well on simulated data.

Significant work remains to improve real-world data performance.

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 on the 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.