Deep-Learning Image Reconstruction for Real-Time Photoacoustic System.
Deep-Learning Image Reconstruction for Real-Time Photoacoustic System.
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DOI：
10.1109/tmi.2020.2993835
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发表时间：
2020-11
影响因子：
10.6
通讯作者：
O'Donnell M
中科院分区：
文献类型：
--
作者：
Kim M;Jeng GS;Pelivanov I;O'Donnell M
光声（PA）成像的最新进展已经能够实现微血管结构的详细图像和血氧或灌注的定量测量。用于PA成像的标准重建方法基于使用适当的信号和系统模型来求解逆问题。然而，对于手持式扫描仪，有限的检测视图和带宽的不适定条件在大多数情况下产生低的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。它被设计用于实时临床应用，并通过模拟典型微血管网络的大规模合成数据进行训练。使用合成和真实的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上级重建。
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.