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- Publisher Website: 10.1364/OSAC.393325
- Scopus: eid_2-s2.0-85105689725
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Article: Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning
Title | Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning |
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Authors | |
Issue Date | 2020 |
Citation | OSA Continuum, 2020, v. 3, n. 6, p. 1664-1678 How to Cite? |
Abstract | Optical coherence tomography angiography (OCTA) requires high transverse sampling density for visualizing retinal and choroidal capillaries. Low transverse sampling causes digital resolution degradation, such as the angiograms in wide-field OCTA. In this paper, we propose to address this problem using deep learning. We conducted extensive experiments on converting the centrally cropped 3 × 3 mm2 field of view (FOV) of the 8 × 8 mm2 foveal OCTA images (a sampling density of 22.9 µm) to the native 3 × 3 mm2 en face OCTA images (a sampling density of 12.2 µm). We employed a cycle-consistent adversarial network architecture in this conversion. The quantitative analysis using the perceptual similarity measures shows the generated OCTA images are closer to the native 3 × 3 mm2 scans. Besides, the results show the proposed method could also enhance the signal-to-noise ratio. We further applied our method to enhance diseased cases and calculate vascular biomarkers, which demonstrates its generalization performance and clinical perspective. |
Persistent Identifier | http://hdl.handle.net/10722/345030 |
DC Field | Value | Language |
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dc.contributor.author | Zhou, Ting | - |
dc.contributor.author | Yang, Jianlong | - |
dc.contributor.author | Zhou, Kang | - |
dc.contributor.author | Fang, Liyang | - |
dc.contributor.author | Hu, Yan | - |
dc.contributor.author | Cheng, Jun | - |
dc.contributor.author | Zhao, Yitian | - |
dc.contributor.author | Chen, Xiangping | - |
dc.contributor.author | Gao, Shenghua | - |
dc.contributor.author | Liu, Jiang | - |
dc.date.accessioned | 2024-08-15T09:24:46Z | - |
dc.date.available | 2024-08-15T09:24:46Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | OSA Continuum, 2020, v. 3, n. 6, p. 1664-1678 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345030 | - |
dc.description.abstract | Optical coherence tomography angiography (OCTA) requires high transverse sampling density for visualizing retinal and choroidal capillaries. Low transverse sampling causes digital resolution degradation, such as the angiograms in wide-field OCTA. In this paper, we propose to address this problem using deep learning. We conducted extensive experiments on converting the centrally cropped 3 × 3 mm2 field of view (FOV) of the 8 × 8 mm2 foveal OCTA images (a sampling density of 22.9 µm) to the native 3 × 3 mm2 en face OCTA images (a sampling density of 12.2 µm). We employed a cycle-consistent adversarial network architecture in this conversion. The quantitative analysis using the perceptual similarity measures shows the generated OCTA images are closer to the native 3 × 3 mm2 scans. Besides, the results show the proposed method could also enhance the signal-to-noise ratio. We further applied our method to enhance diseased cases and calculate vascular biomarkers, which demonstrates its generalization performance and clinical perspective. | - |
dc.language | eng | - |
dc.relation.ispartof | OSA Continuum | - |
dc.title | Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1364/OSAC.393325 | - |
dc.identifier.scopus | eid_2-s2.0-85105689725 | - |
dc.identifier.volume | 3 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 1664 | - |
dc.identifier.epage | 1678 | - |
dc.identifier.eissn | 2578-7519 | - |