June 2020
Volume 61, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2020
Reconstruction of high-resolution 6×6-mm OCT angiogram using deep learning
Author Affiliations & Notes
  • Min Gao
    Casey Eye Institute,OHSU, Portland, Oregon, United States
  • Yukun Guo
    Casey Eye Institute,OHSU, Portland, Oregon, United States
  • Acner Camino
    Casey Eye Institute,OHSU, Portland, Oregon, United States
  • Jiande Sun
    School of Information Science and Engineering, Shandong Normal University, China
  • Thomas S Hwang
    Casey Eye Institute,OHSU, Portland, Oregon, United States
  • Yali Jia
    Casey Eye Institute,OHSU, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Min Gao, None; Yukun Guo, None; Acner Camino, Optovue,Inc. (P); Jiande Sun, None; Thomas Hwang, None; Yali Jia, optovue,Inc. (F), optovue,Inc. (P)
  • Footnotes
    Support  National Institutes of Health (R01 EY027833, R01 EY024544, P30 EY010572); Unrestricted Departmental Funding Grant and William & Mary Greve Special Scholar Award from Research to Prevent Blindness (New York, NY). Natural Science Foundation for Distinguished Young Scholars of Shandong Province (JQ201718). Natural Science Foundation of China (U1736122). Shandong Provincial Key Research and Development Plan (2017CXGC1504).
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 2019. doi:
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    • Get Citation

      Min Gao, Yukun Guo, Acner Camino, Jiande Sun, Thomas S Hwang, Yali Jia; Reconstruction of high-resolution 6×6-mm OCT angiogram using deep learning. Invest. Ophthalmol. Vis. Sci. 2020;61(7):2019.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose : To retrieve flow signal from under-sampled low-quality 6×6-mm optical coherence tomography angiography (OCTA) of the superficial vascular complex (SVC) of the retina using deep learning.

Methods : 6×6-mmand 3×3-mm OCTA scans of the macula were acquired with scanning density of 304×304 lines on one or both eyes of 178 participants (141 diabetic retinopathy (DR), 37 healthy) using a 70-kHz commercial OCT system (Optovue, Inc.). SVC angiograms were generated by maximum projection of the OCTA signal in the slab including nerve fiber layer and ganglion cell layer. A deep residual learning network [Fig. 1] was trained to generate high-resolution 6×6-mm SVC angiograms using the cropped 3×3-mm section of the low-resolution 6×6-mmangiograms as input, and the registered 3×3-mm SVC angiograms of the same eye as ground truth. The loss function used in the learning stage was a linear combination of the mean square error and the structural similarity. On randomly selected 29 DR eyes, we evaluated the high-resolution 6×6-mmangiogram produced by the network for noise intensity in foveal avascular zone, contrast, vascular connectivity within the 3×3-mm section, and false flow signal on the reconstructed images.

Results : Compared to the original 6×6-mm angiograms, the noise intensity of reconstructed images was significantly reduced (29.76 ± 30.57 vs. 0.35 ± 1.55, t-test, p <0.001); the image contrast was significantly improved (0.19 ± 0.00 vs. 0.22 ± 0.00, t-test, p <0.001). Compared to the original 3×3-mm angiogram with proper sampling density,vascular connectivity of the reconstructed cropped 3×3-mm section of 6×6-mm was significantly enhanced (0.90 ± 0.01 vs. 0.98 ± 0.00, t-test, p <0.001) [Fig. 2]. In a noise simulation experiment, we found our algorithm did not generate false flow signal when the noise intensity was under 500, which is far above the noise intensity measured in original 3×3-mm (117.15 ± 97.40) and 6×6-mm (29.76 ± 30.57) angiograms.

Conclusions : Our method can reconstruct high-resolution angiogram from the low-definition 6×6-mm en face OCTA. The enhanced 6×6-mm angiograms may improve the accuracy of disease biomarkers such as non-perfusion area and vessel density.

This is a 2020 ARVO Annual Meeting abstract.




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