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Deep-learning-based motion correction in optical coherence tomography angiography.
J Biophotonics. 2021 Jul 20 [Online ahead of print]JB

Abstract

Optical coherence tomography angiography (OCTA) is a widely applied tool to image microvascular networks with high spatial resolution and sensitivity. Due to limited imaging speed, the artifacts caused by tissue motion can severely compromise visualization of the microvascular networks and quantification of OCTA images. In this article, we propose a deep-learning-based framework to effectively correct motion artifacts and retrieve microvascular architectures. This method comprised two deep neural networks in which the first subnet was applied to distinguish motion corrupted B-scan images from a volumetric dataset. Based on the classification results, the artifacts could be removed from the en face maximum-intensity-projection (MIP) OCTA image. To restore the disturbed vasculature induced by artifact removal, the second subnet, an inpainting neural network, was utilized to reconnect the broken vascular networks. We applied the method to postprocess OCTA images of the microvascular networks in mouse cortex in vivo. Both image comparison and quantitative analysis show that the proposed method can significantly improve OCTA image by efficiently recovering microvasculature from the overwhelming motion artifacts.

Authors+Show Affiliations

Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

34288527

Citation

Li, Ang, et al. "Deep-learning-based Motion Correction in Optical Coherence Tomography Angiography." Journal of Biophotonics, 2021, pp. e202100097.
Li A, Du C, Pan Y. Deep-learning-based motion correction in optical coherence tomography angiography. J Biophotonics. 2021.
Li, A., Du, C., & Pan, Y. (2021). Deep-learning-based motion correction in optical coherence tomography angiography. Journal of Biophotonics, e202100097. https://doi.org/10.1002/jbio.202100097
Li A, Du C, Pan Y. Deep-learning-based Motion Correction in Optical Coherence Tomography Angiography. J Biophotonics. 2021 Jul 20;e202100097. PubMed PMID: 34288527.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Deep-learning-based motion correction in optical coherence tomography angiography. AU - Li,Ang, AU - Du,Congwu, AU - Pan,Yingtian, Y1 - 2021/07/20/ PY - 2021/07/18/revised PY - 2021/03/22/received PY - 2021/07/19/accepted PY - 2021/7/22/pubmed PY - 2021/7/22/medline PY - 2021/7/21/entrez KW - OCTA KW - deep neural networks KW - microvascular network KW - motion correction SP - e202100097 EP - e202100097 JF - Journal of biophotonics JO - J Biophotonics N2 - Optical coherence tomography angiography (OCTA) is a widely applied tool to image microvascular networks with high spatial resolution and sensitivity. Due to limited imaging speed, the artifacts caused by tissue motion can severely compromise visualization of the microvascular networks and quantification of OCTA images. In this article, we propose a deep-learning-based framework to effectively correct motion artifacts and retrieve microvascular architectures. This method comprised two deep neural networks in which the first subnet was applied to distinguish motion corrupted B-scan images from a volumetric dataset. Based on the classification results, the artifacts could be removed from the en face maximum-intensity-projection (MIP) OCTA image. To restore the disturbed vasculature induced by artifact removal, the second subnet, an inpainting neural network, was utilized to reconnect the broken vascular networks. We applied the method to postprocess OCTA images of the microvascular networks in mouse cortex in vivo. Both image comparison and quantitative analysis show that the proposed method can significantly improve OCTA image by efficiently recovering microvasculature from the overwhelming motion artifacts. SN - 1864-0648 UR - https://www.unboundmedicine.com/medline/citation/34288527/Deep_learning_based_motion_correction_in_optical_coherence_tomography_angiography_ L2 - https://doi.org/10.1002/jbio.202100097 DB - PRIME DP - Unbound Medicine ER -
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