# Deep Learning in Photoacoustic Tomography: Current approaches and future directions

arXiv: https://arxiv.org/abs/2009.07608 · HTML: https://mlsanity.com/paper/2009.07608/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 25 of 25)

- [Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems](https://mlsanity.com/paper/2212.03130/) (2022)
- [Deep Bayesian inference for seismic imaging with tasks](https://mlsanity.com/paper/2110.04825/) (2021)
- [Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography](https://mlsanity.com/paper/2304.01963/) (2023)
- [Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography](https://mlsanity.com/paper/2204.06270/) (2022)
- [Semantic segmentation of multispectral photoacoustic images using deep learning](https://mlsanity.com/paper/2105.09624/) (2021)
- [Sliding Gaussian ball adaptive growth (SlingBAG): point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging](https://mlsanity.com/paper/2407.11781/) (2024)
- [AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data](https://mlsanity.com/paper/2101.08934/) (2021)
- [Photoacoustic image synthesis with generative adversarial networks](https://mlsanity.com/paper/2103.15510/) (2021)
- [On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector](https://mlsanity.com/paper/2204.10001/) (2022)
- [Dense Dilated UNet: Deep Learning for 3D Photoacoustic Tomography Image Reconstruction](https://mlsanity.com/paper/2104.03130/) (2021)
- [Diffusion assisted image reconstruction in optoacoustic tomography](https://mlsanity.com/paper/2404.10239/) (2024)
- [Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review](https://mlsanity.com/paper/2405.13832/) (2024)
- [Joint Segmentation and Image Reconstruction with Error Prediction in Photoacoustic Imaging using Deep Learning](https://mlsanity.com/paper/2407.02653/) (2024)
- [Fast algorithms enabling optimization and deep learning for photoacoustic tomography in a circular detection geometry](https://mlsanity.com/paper/2510.24687/) (2025)
- [Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue](https://mlsanity.com/paper/2102.12960/) (2021)
- [Compressed Sensing for Photoacoustic Computed Tomography Using an Untrained Neural Network](https://mlsanity.com/paper/2105.14255/) (2021)
- [Deep Learning Adapted Acceleration for Limited-view Photoacoustic Computed Tomography](https://mlsanity.com/paper/2111.05194/) (2021)
- [Cross-domain Self-supervised Framework for Photoacoustic Computed Tomography Image Reconstruction](https://mlsanity.com/paper/2301.06681/) (2023)
- [Learning end-to-end inversion of circular Radon transforms in the partial radial setup](https://mlsanity.com/paper/2308.14144/) (2023)
- [Design, Implementation and Analysis of a Compressed Sensing Photoacoustic Projection Imaging System](https://mlsanity.com/paper/2402.15750/) (2024)
- [Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications](https://mlsanity.com/paper/2411.02843/) (2024)
- [Using Spatial Diffusions for Optoacoustic Tomography Image Reconstruction](https://mlsanity.com/paper/2411.15156/) (2024)
- [Photoacoustic Iterative Optimization Algorithm with Shape Prior Regularization](https://mlsanity.com/paper/2412.00705/) (2024)
- [Benchmarking Deep Learning-Based Reconstruction Methods for Photoacoustic Computed Tomography with Clinically Relevant Synthetic Datasets](https://mlsanity.com/paper/2601.17165/) (2026)
- [Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts](https://mlsanity.com/paper/2604.19176/) (2026)

## Abstract (arXiv)

Biomedical photoacoustic tomography, which can provide high resolution 3D soft tissue images based on the optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is challenging and leads to slow algorithms. Recently, the application of Deep Learning, or deep neural networks, to this problem has received a great deal of attention. This paper reviews the literature on learned image reconstruction, summarising the current trends, and explains how these new approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods. In particular, it shows how these new techniques can be understood from a Bayesian perspective, providing useful insights. The paper also provides a concise tutorial demonstration of three prototypical approaches to learned image reconstruction. The code and data sets for these demonstrations are available to researchers. It is anticipated that it is in in vivo applications - where data may be sparse, fast imaging critical and priors difficult to construct by hand - that Deep Learning will have the most impact. With this in mind, the paper concludes with some indications of possible future research directions.