In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. First, we performed simulation studies to evaluate the feasibility and effectiveness of the proposed method. Second, we employed this method to process images of rat brain vessels with multiple motion artifacts to evaluate its performance for in vivo applications. The results demonstrate that this method works well for both large blood vessels and capillary networks. In comparison with traditional methods, the proposed method in this study can be easily modified to satisfy different scenarios of motion corrections in OR-PAM by revising the training sets.

中文翻译：

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光学分辨率光声显微镜中基于深度学习的运动校正算法

在这项研究中，我们提出了一种基于深度学习的方法来校正光学分辨率光声显微镜（OR-PAM）中的运动伪像。该方法是卷积神经网络，可从输入的原始数据和运动伪像建立端到端的映射，以输出校正后的图像。首先，我们进行了仿真研究，以评估该方法的可行性和有效性。其次，我们采用这种方法处理具有多个运动伪影的大鼠脑血管图像，以评估其在体内应用的性能。结果表明，该方法适用于大血管和毛细血管网。与传统方法相比，通过修改训练集，可以轻松修改本研究中提出的方法，以满足OR-PAM中不同的运动校正方案。