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Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe
Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, high-speed, high-sensitive volumetric biological imaging in vivo. However, systemic features inherent in the basic operating principle of OCT limit its imaging performance such as spatial resolution and...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147647/ https://www.ncbi.nlm.nih.gov/pubmed/37117279 http://dx.doi.org/10.1038/s42003-023-04846-7 |
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author | Lee, Woojin Nam, Hyeong Soo Seok, Jae Yeon Oh, Wang-Yuhl Kim, Jin Won Yoo, Hongki |
author_facet | Lee, Woojin Nam, Hyeong Soo Seok, Jae Yeon Oh, Wang-Yuhl Kim, Jin Won Yoo, Hongki |
author_sort | Lee, Woojin |
collection | PubMed |
description | Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, high-speed, high-sensitive volumetric biological imaging in vivo. However, systemic features inherent in the basic operating principle of OCT limit its imaging performance such as spatial resolution and signal-to-noise ratio. Here, we propose a deep learning-based OCT image enhancement framework that exploits raw interference fringes to achieve further enhancement from currently obtainable optimized images. The proposed framework for enhancing spatial resolution and reducing speckle noise in OCT images consists of two separate models: an A-scan-based network (NetA) and a B-scan-based network (NetB). NetA utilizes spectrograms obtained via short-time Fourier transform of raw interference fringes to enhance axial resolution of A-scans. NetB was introduced to enhance lateral resolution and reduce speckle noise in B-scan images. The individually trained networks were applied sequentially. We demonstrate the versatility and capability of the proposed framework by visually and quantitatively validating its robust performance. Comparative studies suggest that deep learning utilizing interference fringes can outperform the existing methods. Furthermore, we demonstrate the advantages of the proposed method by comparing our outcomes with multi-B-scan averaged images and contrast-adjusted images. We expect that the proposed framework will be a versatile technology that can improve functionality of OCT. |
format | Online Article Text |
id | pubmed-10147647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101476472023-04-30 Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe Lee, Woojin Nam, Hyeong Soo Seok, Jae Yeon Oh, Wang-Yuhl Kim, Jin Won Yoo, Hongki Commun Biol Article Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, high-speed, high-sensitive volumetric biological imaging in vivo. However, systemic features inherent in the basic operating principle of OCT limit its imaging performance such as spatial resolution and signal-to-noise ratio. Here, we propose a deep learning-based OCT image enhancement framework that exploits raw interference fringes to achieve further enhancement from currently obtainable optimized images. The proposed framework for enhancing spatial resolution and reducing speckle noise in OCT images consists of two separate models: an A-scan-based network (NetA) and a B-scan-based network (NetB). NetA utilizes spectrograms obtained via short-time Fourier transform of raw interference fringes to enhance axial resolution of A-scans. NetB was introduced to enhance lateral resolution and reduce speckle noise in B-scan images. The individually trained networks were applied sequentially. We demonstrate the versatility and capability of the proposed framework by visually and quantitatively validating its robust performance. Comparative studies suggest that deep learning utilizing interference fringes can outperform the existing methods. Furthermore, we demonstrate the advantages of the proposed method by comparing our outcomes with multi-B-scan averaged images and contrast-adjusted images. We expect that the proposed framework will be a versatile technology that can improve functionality of OCT. Nature Publishing Group UK 2023-04-28 /pmc/articles/PMC10147647/ /pubmed/37117279 http://dx.doi.org/10.1038/s42003-023-04846-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lee, Woojin Nam, Hyeong Soo Seok, Jae Yeon Oh, Wang-Yuhl Kim, Jin Won Yoo, Hongki Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe |
title | Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe |
title_full | Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe |
title_fullStr | Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe |
title_full_unstemmed | Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe |
title_short | Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe |
title_sort | deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147647/ https://www.ncbi.nlm.nih.gov/pubmed/37117279 http://dx.doi.org/10.1038/s42003-023-04846-7 |
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