Recent advances in photoacoustic (PA) imaging have enabled detailed images of microvascular structure and quantitative measurement of blood oxygenation or perfusion. Standard reconstruction methods for PA imaging are based on solving an inverse problem using appropriate signal and system models. For handheld scanners, however, the ill-posed conditions of limited detection view and bandwidth yield low image contrast and severe structure loss in most instances. In this paper, we propose a practical reconstruction method based on a deep convolutional neural network (CNN) to overcome those problems. It is designed for real-time clinical applications and trained by large-scale synthetic data mimicking typical microvessel networks. Experimental results using synthetic and real datasets confirm that the deep-learning approach provides superior reconstructions compared to conventional methods.

中文翻译：

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实时光声系统的深度学习图像重建


光声 (PA) 成像的最新进展使得微血管结构的详细图像和血氧或灌注的定量测量成为可能。 PA 成像的标准重建方法基于使用适当的信号和系统模型解决反演问题。然而，对于手持式扫描仪来说，有限的检测视野和带宽的不适定条件在大多数情况下会产生低图像对比度和严重的结构损失。在本文中，我们提出了一种基于深度卷积神经网络（CNN）的实用重建方法来克服这些问题。它专为实时临床应用而设计，并通过模仿典型微血管网络的大规模合成数据进行训练。使用合成数据集和真实数据集的实验结果证实，与传统方法相比，深度学习方法提供了卓越的重建效果。