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.

光声成像（PAI）是一种有前途的新兴成像方式，能够对组织深处几厘米的光学组织特性进行空间分辨成像，为许多令人兴奋的临床应用创造了潜力。然而，从原始数据中提取相关组织参数需要解决逆图像重建问题，这已被证明极其难以解决。深度学习方法的应用最近迅速普及，在医学成像领域取得了令人瞩目的成功，并首次在 PAI 领域得到应用。深度学习方法具有独特的优势，可以促进 PAI 的临床转化，例如极快的计算时间以及它们可以适应任何给定问题的事实。在这篇综述中，我们研究了 PAI 深度学习的最新技术水平，并确定了有助于实现临床适用性目标的潜在研究方向。