# Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach

## TL;DR

This paper introduces a deep learning-based method for simultaneously reconstructing initial pressure and sound speed in photoacoustic tomography, overcoming the challenge of unknown sound speed distribution and improving image quality.

## Contribution

A novel data-driven approach combining deep neural networks with model-based iteration for joint reconstruction in photoacoustic tomography.

## Key findings

- Significant improvement in initial pressure image quality in simulations.
- Effective simultaneous reconstruction of pressure and sound speed.
- Potential to enhance practical photoacoustic imaging applications.

## Abstract

Photoacoustic tomography seeks to reconstruct an acoustic initial pressure distribution from the measurement of the ultrasound waveforms. Conventional methods assume a-prior knowledge of the sound speed distribution, which practically is unknown. One way to circumvent the issue is to simultaneously reconstruct both the acoustic initial pressure and speed. In this article, we develop a novel data-driven method that integrates an advanced deep neural network through model-based iteration. The image of the initial pressure is significantly improved in our numerical simulation.