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

arXiv: https://arxiv.org/abs/1907.09951 · HTML: https://mlsanity.com/paper/1907.09951/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography](https://mlsanity.com/paper/2204.06270/) (2022)
- [On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector](https://mlsanity.com/paper/2204.10001/) (2022)
- [Coordinate-based Speed of Sound Recovery for Aberration-Corrected Photoacoustic Computed Tomography](https://mlsanity.com/paper/2409.10876/) (2024)
- [Cross-domain Self-supervised Framework for Photoacoustic Computed Tomography Image Reconstruction](https://mlsanity.com/paper/2301.06681/) (2023)
- [Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications](https://mlsanity.com/paper/2411.02843/) (2024)

## Abstract (arXiv)

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.