Liao, J., Yang, S. orcid.org/0000-0003-0531-2903, Zhang, T. et al. (2 more authors) (2023) Fast optical coherence tomography angiography image acquisition and reconstruction pipeline for skin application. Biomedical Optics Express, 14 (8). pp. 3899-3913. ISSN 2156-7085
Abstract
Traditional high-quality OCTA images require multi-repeated scans (e.g., 4-8 repeats) in the same position, which may cause the patient to be uncomfortable. We propose a deep-learning-based pipeline that can extract high-quality OCTA images from only two-repeat OCT scans. The performance of the proposed image reconstruction U-Net (IRU-Net) outperforms the state-of-the-art UNet vision transformer and UNet in OCTA image reconstruction from a two-repeat OCT signal. The results demonstrated a mean peak-signal-to-noise ratio increased from 15.7 to 24.2; the mean structural similarity index measure improved from 0.28 to 0.59, while the OCT data acquisition time was reduced from 21 seconds to 3.5 seconds (reduced by 83%).
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement. Reproduced in accordance with the publisher's self-archiving policy. |
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 09:45 |
Last Modified: | 21 Aug 2024 09:45 |
Status: | Published |
Publisher: | Optica Publishing Group |
Identification Number: | 10.1364/boe.486933 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:216305 |