# Deep Learning for Photoacoustic Tomography from Sparse Data

## TL;DR

This paper introduces a deep learning-based reconstruction algorithm for photoacoustic tomography from sparse data, achieving high-quality images efficiently by combining filtered backprojection and U-net architecture.

## Contribution

The paper presents a novel deep learning approach that integrates traditional filtered backprojection with a U-net for fast, accurate image reconstruction in sparse data PAT.

## Key findings

- Reconstruction quality comparable to iterative methods.
- Significantly faster image reconstruction process.
- Effective handling of sparse data in PAT.

## Abstract

The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacoustic tomography (PAT). We develop a direct and highly efficient reconstruction algorithm based on deep learning. In our approach image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data. The proposed reconstruction approach can be interpreted as a network that uses the PAT filtered backprojection algorithm for the first layer, followed by the U-net architecture for the remaining layers. Actual image reconstruction with deep learning consists in one evaluation of the trained CNN, which does not require time consuming solution of the forward and adjoint problems. At the same time, our numerical results demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative approaches for PAT from sparse data.