论文标题
生物医学光声成像的深度学习：评论
Deep learning for biomedical photoacoustic imaging: A review
论文作者
论文摘要
光声成像（PAI）是一种有希望的新兴成像模态，可实现直至组织深度几厘米的光学组织特性的空间分辨成像，从而为众多令人兴奋的临床应用提供了潜力。但是，从原始数据中提取相关的组织参数需要解决反图像重建问题，事实证明这很难解决。深度学习方法的应用最近在受欢迎程度上爆炸，在医学成像的背景下取得了令人印象深刻的成功，并且在PAI领域首次使用。深度学习方法具有独特的优势，可以促进PAI的临床翻译，例如非常快速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，我们研究了有关PAI深度学习的当前最新技术状态，并确定了潜在的研究方向，这将有助于实现临床适用性的目标
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
