Significance: Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding. Aim: We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities. Approach: Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI. Results: When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis. Conclusion: DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.

中文翻译：

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光声成像中的深度学习：综述


意义：光声（PA）成像可以为临床前和临床研究提供结构、功能和分子信息。对于 PA 成像 (PAI)，非理想信号检测会降低图像质量，而由于深层组织中未知的光注量光谱，定量 PAI (QPAI) 仍然具有挑战性。近年来，深度学习（DL）在 PAI 中实现时表现出了出色的性能，在图像重建、量化和理解方面都有应用。目标：我们提供（i）对 PAI 中应用的深度学习技术的全面概述，（ii）为各种 PAI 任务设计深度学习模型的参考，以及（iii）对未来挑战和机遇的总结。方法：对 2020 年 11 月之前发表的在 PAI 中应用深度学习领域的论文进行审查。我们将它们分为三种类型：图像理解、初始压力分布重建和QPAI。结果：DL应用于PAI时，可以有效处理图像、提高重建质量、融合信息、辅助定量分析。结论：DL 已成为 PAI 中的强大工具。随着DL理论和技术的发展，它将不断提升PAI的性能并促进其临床转化。