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Cited 4 time in webofscience Cited 5 time in scopus
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Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy SCIE SCOPUS

Title
Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy
Authors
Kim, JongbeomLee, DongyoonLim, HyokyungYang, HyekyeongKim, JaewooKim, JeesuKim, YeonggeunKim, Hyung HamKim, Chulhong
Date Issued
2022-12
Publisher
Nature Research
Abstract
© 2022, The Author(s).Simultaneous point-by-point raster scanning of optical and acoustic beams has been widely adapted to high-speed photoacoustic microscopy (PAM) using a water-immersible microelectromechanical system or galvanometer scanner. However, when using high-speed water-immersible scanners, the two consecutively acquired bidirectional PAM images are misaligned with each other because of unstable performance, which causes a non-uniform time interval between scanning points. Therefore, only one unidirectionally acquired image is typically used; consequently, the imaging speed is reduced by half. Here, we demonstrate a scanning framework based on a deep neural network (DNN) to correct misaligned PAM images acquired via bidirectional raster scanning. The proposed method doubles the imaging speed compared to that of conventional methods by aligning nonlinear mismatched cross-sectional B-scan photoacoustic images during bidirectional raster scanning. Our DNN-assisted raster scanning framework can further potentially be applied to other raster scanning-based biomedical imaging tools, such as optical coherence tomography, ultrasound microscopy, and confocal microscopy.
URI
https://oasis.postech.ac.kr/handle/2014.oak/116558
DOI
10.1038/s41598-022-20378-2
ISSN
2045-2322
Article Type
Article
Citation
Scientific Reports, vol. 12, no. 1, 2022-12
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김철홍KIM, CHULHONG
Dept of Electrical Enginrg
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