# Photoacoustic image reconstruction via deep learning

## TL;DR

This paper introduces a deep learning approach for photoacoustic image reconstruction that produces high-quality images from sparse data efficiently, outperforming traditional iterative methods in speed and robustness.

## Contribution

The paper presents a novel direct deep learning-based reconstruction algorithm that bypasses iterative methods, reducing computation time and dependency on prior assumptions.

## Key findings

- Deep learning achieves comparable image quality to iterative methods.
- Reconstruction is significantly faster with a single network evaluation.
- The approach is robust to sparse data scenarios.

## Abstract

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.