Liao, J., Yang, S. orcid.org/0000-0003-0531-2903, Zhang, T. et al. (2 more authors) (2023) A hand-held optical coherence tomography angiography scanner based on angiography reconstruction transformer networks. Journal of Biophotonics, 16 (9). e202300100. ISSN 1864-063X
Abstract
Optical coherence tomography angiography (OCTA) has successfully demonstrated its viability for clinical applications in dermatology. Due to the high optical scattering property of skin, extracting high-quality OCTA images from skin tissues requires at least six-repeated scans. While the motion artifacts from the patient and the free hand-held probe can lead to a low-quality OCTA image. Our deep-learning-based scan pipeline enables fast and high-quality OCTA imaging with 0.3-s data acquisition. We utilize a fast scanning protocol with a 60 μm/pixel spatial interval rate and introduce angiography-reconstruction-transformer (ART) for 4× super-resolution of low transverse resolution OCTA images. The ART outperforms state-of-the-art networks in OCTA image super-resolution and provides a lighter network size. ART can restore microvessels while reducing the processing time by 85%, and maintaining improvements in structural similarity and peak-signal-to-noise ratio. This study represents that ART can achieve fast and flexible skin OCTA imaging while maintaining image quality.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | deep learning, optical coherence tomography angiography, single image super-resolution |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Aug 2024 09:59 |
Last Modified: | 21 Aug 2024 09:59 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/jbio.202300100 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:216308 |