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High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning
Significance: Reducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit depth will lead to the degradation of the detection sensitivity, t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society of Photo-Optical Instrumentation Engineers
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666869/ https://www.ncbi.nlm.nih.gov/pubmed/33191687 http://dx.doi.org/10.1117/1.JBO.25.12.123702 |
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author | Hao, Qiangjiang Zhou, Kang Yang, Jianlong Hu, Yan Chai, Zhengjie Ma, Yuhui Liu, Gangjun Zhao, Yitian Gao, Shenghua Liu, Jiang |
author_facet | Hao, Qiangjiang Zhou, Kang Yang, Jianlong Hu, Yan Chai, Zhengjie Ma, Yuhui Liu, Gangjun Zhao, Yitian Gao, Shenghua Liu, Jiang |
author_sort | Hao, Qiangjiang |
collection | PubMed |
description | Significance: Reducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit depth will lead to the degradation of the detection sensitivity, thus reducing the signal-to-noise ratio (SNR) of OCT images. Aim: We propose using deep learning to reconstruct high SNR OCT images from low bit-depth acquisition. Approach: The feasibility of our approach is evaluated by applying this approach to the quantized 3- to 8-bit data from native 12-bit interference fringes. We employ a pixel-to-pixel generative adversarial network (pix2pixGAN) architecture in the low-to-high bit-depth OCT image transition. Results: Extensively, qualitative and quantitative results show our method could significantly improve the SNR of the low bit-depth OCT images. The adopted pix2pixGAN is superior to other possible deep learning and compressed sensing solutions. Conclusions: Our work demonstrates that the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings. |
format | Online Article Text |
id | pubmed-7666869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Society of Photo-Optical Instrumentation Engineers |
record_format | MEDLINE/PubMed |
spelling | pubmed-76668692020-11-23 High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning Hao, Qiangjiang Zhou, Kang Yang, Jianlong Hu, Yan Chai, Zhengjie Ma, Yuhui Liu, Gangjun Zhao, Yitian Gao, Shenghua Liu, Jiang J Biomed Opt Special Series on Biomedical Imaging and Sensing Significance: Reducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit depth will lead to the degradation of the detection sensitivity, thus reducing the signal-to-noise ratio (SNR) of OCT images. Aim: We propose using deep learning to reconstruct high SNR OCT images from low bit-depth acquisition. Approach: The feasibility of our approach is evaluated by applying this approach to the quantized 3- to 8-bit data from native 12-bit interference fringes. We employ a pixel-to-pixel generative adversarial network (pix2pixGAN) architecture in the low-to-high bit-depth OCT image transition. Results: Extensively, qualitative and quantitative results show our method could significantly improve the SNR of the low bit-depth OCT images. The adopted pix2pixGAN is superior to other possible deep learning and compressed sensing solutions. Conclusions: Our work demonstrates that the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings. Society of Photo-Optical Instrumentation Engineers 2020-11-15 2020-12 /pmc/articles/PMC7666869/ /pubmed/33191687 http://dx.doi.org/10.1117/1.JBO.25.12.123702 Text en © 2020 The Authors https://creativecommons.org/licenses/by/4.0/ Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. |
spellingShingle | Special Series on Biomedical Imaging and Sensing Hao, Qiangjiang Zhou, Kang Yang, Jianlong Hu, Yan Chai, Zhengjie Ma, Yuhui Liu, Gangjun Zhao, Yitian Gao, Shenghua Liu, Jiang High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning |
title | High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning |
title_full | High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning |
title_fullStr | High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning |
title_full_unstemmed | High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning |
title_short | High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning |
title_sort | high signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning |
topic | Special Series on Biomedical Imaging and Sensing |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666869/ https://www.ncbi.nlm.nih.gov/pubmed/33191687 http://dx.doi.org/10.1117/1.JBO.25.12.123702 |
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