Deep learning for biomedical photoacoustic imaging: A review

Abstract

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

arXiv comment31 pages, 8 figures, 3 tables, 169 references

References in corpus (7)

• Model based learning for accelerated, limited-view 3D photoacoustic tomography
• Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
• Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging
• Context encoding enables machine learning-based quantitative photoacoustics
• A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation
• Deep learning versus ${\ell }^1$-minimization for compressed sensing photoacoustic tomography
• Quantitative reconstructions in multi-modal photoacoustic and optical coherence tomography imaging

Cited by in corpus (7)

• Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)
• Semantic segmentation of multispectral photoacoustic images using deep learning
• Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets
• A study of why we need to reassess full reference image quality assessment with medical images
• Photoacoustic image synthesis with generative adversarial networks
• Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography
• Unsupervised Domain Transfer with Conditional Invertible Neural Networks