# Deep learning for biomedical photoacoustic imaging: A review

arXiv: https://arxiv.org/abs/2011.02744 · HTML: https://mlsanity.com/paper/2011.02744/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 21 of 21)

- [A study of why we need to reassess full reference image quality assessment with medical images](https://mlsanity.com/paper/2405.19097/) (2024)
- [Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms](https://mlsanity.com/paper/2306.06748/) (2023)
- [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)
- [Unsupervised Domain Transfer with Conditional Invertible Neural Networks](https://mlsanity.com/paper/2303.10191/) (2023)
- [Digital twins enable full-reference quality assessment of photoacoustic image reconstructions](https://mlsanity.com/paper/2505.24514/) (2025)
- [Photoacoustic image synthesis with generative adversarial networks](https://mlsanity.com/paper/2103.15510/) (2021)
- [Limited-View Photoacoustic Imaging Reconstruction Via High-quality Self-supervised Neural Representation](https://mlsanity.com/paper/2407.03663/) (2024)
- [Dense Dilated UNet: Deep Learning for 3D Photoacoustic Tomography Image Reconstruction](https://mlsanity.com/paper/2104.03130/) (2021)
- [Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry](https://mlsanity.com/paper/2403.14863/) (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)
- [Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging](https://mlsanity.com/paper/2201.11630/) (2022)
- [Pre-screening breast cancer with machine learning and deep learning](https://mlsanity.com/paper/2302.02406/) (2023)
- [Design, Implementation and Analysis of a Compressed Sensing Photoacoustic Projection Imaging System](https://mlsanity.com/paper/2402.15750/) (2024)
- [PhotIQA: A photoacoustic image data set with image quality ratings](https://mlsanity.com/paper/2507.03478/) (2025)
- [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)
- [Dual-Domain Self-Supervised Artifact Removal Framework for Photoacoustic Computed Tomography](https://mlsanity.com/paper/2607.16304/) (2026)

## Abstract (arXiv)

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem. In this review, we examine the current state of the art regarding deep learning in PAI and identify potential directions of research that will help to reach the goal of clinical applicability