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Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality
PURPOSE: To investigate the impact of denoising on the qualitative and quantitative parameters of optical coherence tomography angiography (OCTA) images of the optic nerve and macular area. METHODS: OCTA images of the optic nerve and macular area were obtained using a Canon-HS100 OCT device for 48 p...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8221851/ https://www.ncbi.nlm.nih.gov/pubmed/34221184 http://dx.doi.org/10.1155/2021/1373362 |
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author | Xu, Yishuang Su, Yu Hua, Dihao Heiduschka, Peter Zhang, Wenliang Cao, Tianyue Liu, Jingcheng Ji, Zhenyu Eter, Nicole |
author_facet | Xu, Yishuang Su, Yu Hua, Dihao Heiduschka, Peter Zhang, Wenliang Cao, Tianyue Liu, Jingcheng Ji, Zhenyu Eter, Nicole |
author_sort | Xu, Yishuang |
collection | PubMed |
description | PURPOSE: To investigate the impact of denoising on the qualitative and quantitative parameters of optical coherence tomography angiography (OCTA) images of the optic nerve and macular area. METHODS: OCTA images of the optic nerve and macular area were obtained using a Canon-HS100 OCT device for 48 participants (48 eyes). Multiple image averaging (MIA) and denoising techniques were used to improve the quality of the OCTA images. The peak signal-to-noise ratio (PSNR) as an image quality parameter and vessel density (VD) as a quantitative parameter were obtained from single-scan, MIA, and denoised OCTA images. The parameters were compared, and the correlation was analyzed between different imaging protocols. RESULTS: In the optic nerve area, there were significant differences in the PSNR and VD in all measured regions between the three groups (P < 0.0001). The PSNR of the denoised group was significantly higher than that of the other two groups (P < 0.0001). The VD in the denoised group was significantly lower than that in the single-scan group in all measured regions (P < 0.0001). In the macular area, there were significant differences in the PSNR and VD in all measured regions among the three groups. The PSNR of the denoised group was significantly higher than that of the other two groups (P < 0.0001). The VD in the denoised group was significantly lower than that in the single-scan group in all measured regions. The VD around the optic nerve in the denoised group was correlated with that in the single-scan group (R = 0.9403, P < 0.0001), but the VD in the MIA group was not correlated with that in the single-scan group (R = 0.2505, P = 0.2076). The VD around the fovea in the denoised and MIA images was correlated with that in the single-scan group (R = 0.7377, P < 0.0001; R = 0.7005, P = 0.0004, respectively). CONCLUSION: Denoising could provide an easy and quick way to improve image quality parameters, such as PSNR. It shows great potential in improving the sensitivity of OCTA images as retinal disease markers. |
format | Online Article Text |
id | pubmed-8221851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-82218512021-07-02 Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality Xu, Yishuang Su, Yu Hua, Dihao Heiduschka, Peter Zhang, Wenliang Cao, Tianyue Liu, Jingcheng Ji, Zhenyu Eter, Nicole Dis Markers Research Article PURPOSE: To investigate the impact of denoising on the qualitative and quantitative parameters of optical coherence tomography angiography (OCTA) images of the optic nerve and macular area. METHODS: OCTA images of the optic nerve and macular area were obtained using a Canon-HS100 OCT device for 48 participants (48 eyes). Multiple image averaging (MIA) and denoising techniques were used to improve the quality of the OCTA images. The peak signal-to-noise ratio (PSNR) as an image quality parameter and vessel density (VD) as a quantitative parameter were obtained from single-scan, MIA, and denoised OCTA images. The parameters were compared, and the correlation was analyzed between different imaging protocols. RESULTS: In the optic nerve area, there were significant differences in the PSNR and VD in all measured regions between the three groups (P < 0.0001). The PSNR of the denoised group was significantly higher than that of the other two groups (P < 0.0001). The VD in the denoised group was significantly lower than that in the single-scan group in all measured regions (P < 0.0001). In the macular area, there were significant differences in the PSNR and VD in all measured regions among the three groups. The PSNR of the denoised group was significantly higher than that of the other two groups (P < 0.0001). The VD in the denoised group was significantly lower than that in the single-scan group in all measured regions. The VD around the optic nerve in the denoised group was correlated with that in the single-scan group (R = 0.9403, P < 0.0001), but the VD in the MIA group was not correlated with that in the single-scan group (R = 0.2505, P = 0.2076). The VD around the fovea in the denoised and MIA images was correlated with that in the single-scan group (R = 0.7377, P < 0.0001; R = 0.7005, P = 0.0004, respectively). CONCLUSION: Denoising could provide an easy and quick way to improve image quality parameters, such as PSNR. It shows great potential in improving the sensitivity of OCTA images as retinal disease markers. Hindawi 2021-06-16 /pmc/articles/PMC8221851/ /pubmed/34221184 http://dx.doi.org/10.1155/2021/1373362 Text en Copyright © 2021 Yishuang Xu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Xu, Yishuang Su, Yu Hua, Dihao Heiduschka, Peter Zhang, Wenliang Cao, Tianyue Liu, Jingcheng Ji, Zhenyu Eter, Nicole Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality |
title | Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality |
title_full | Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality |
title_fullStr | Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality |
title_full_unstemmed | Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality |
title_short | Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality |
title_sort | enhanced visualization of retinal microvasculature via deep learning on octa image quality |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8221851/ https://www.ncbi.nlm.nih.gov/pubmed/34221184 http://dx.doi.org/10.1155/2021/1373362 |
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