Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy

查看全文 作　　者：Xingxing [1]Chen;Weizhi [2]Qi;Lei [2]Xi

机构地区：[1]School of Electronic Science and Engineering,University of Electronic Science and Technology of China,Chengdu 610054,Sichuan,China;[2]Department of Biomedical Engineering,Southern University of Science and Technology,Shenzhen 518055,Guangdong,China

出　　处：《Visual Computing for Industry,Biomedicine,and Art》索引2019年第2卷第1期,共6页

基　　金：This work was sponsored by National Natural Science Foundation of China,Nos.81571722,61775028 and 61528401. 摘　　要：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. 关键词：Deep learning Optical resolution photoacoustic microscopy Motion correction

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Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy

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