June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Reconstruction of high-resolution OCT angiograms of retinal intermediate and deep capillary plexuses using deep learning
Author Affiliations & Notes
  • Min Gao
    Oregon Health & Science University, Portland, Oregon, United States
  • Tristan T. Hormel
    Oregon Health & Science University, Portland, Oregon, United States
  • Jie Wang
    Oregon Health & Science University, Portland, Oregon, United States
  • Yukun Guo
    Oregon Health & Science University, Portland, Oregon, United States
  • Steven Bailey
    Oregon Health & Science University, Portland, Oregon, United States
  • Thomas S Hwang
    Oregon Health & Science University, Portland, Oregon, United States
  • Yali Jia
    Oregon Health & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Min Gao, None; Tristan Hormel, None; Jie Wang, None; Yukun Guo, None; Steven Bailey, None; Thomas Hwang, None; Yali Jia, Optovue (F), Optovue (P)
  • Footnotes
    Support  National Institutes of Health Grant 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).
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 1032. doi:
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    • Get Citation

      Min Gao, Tristan T. Hormel, Jie Wang, Yukun Guo, Steven Bailey, Thomas S Hwang, Yali Jia; Reconstruction of high-resolution OCT angiograms of retinal intermediate and deep capillary plexuses using deep learning. Invest. Ophthalmol. Vis. Sci. 2021;62(8):1032.

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

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Abstract

Purpose : We propose to use deep learning to reconstruct flow signal from under-sampled 6×6-mm optical coherence tomographic angiography (OCTA) images of the intermediate capillary plexus (ICP) and deep capillary plexus (DCP).

Methods : 6×6-mm macular scans with a 400×400 A-line sampling density and 3×3-mm scans with a 304×304 A-line sampling density were acquired on one or both eyes of 180 participants (including 230 eyes with diabetic retinopathy (DR) and 44 healthy controls) using a 70-kHz commercial OCT system (RTVue-XR; Optovue, Inc.). Projection-resolved OCTA algorithm was applied to remove projection artifacts in voxel. ICP and DCP angiograms were generated by maximum projection of the OCTA signal within the relevant plexus. We proposed a deep-learning-based method, dubbed “deep capillary angiograms reconstruction network” (DCARnet), to reconstruct 6×6-mm high-resolution ICP and DCP en face OCTA images from sparsely-sampled, low-resolution scans of the same area. DCARnet takes registered 3×3-mm ICP and DCP angiograms with proper sampling density as the ground truth reference. Same network can also be applied on 3×3-mm angiograms. We evaluated the reconstructed 3×3- and 6×6-mm angiograms based on vessel connectivity, false flow signal (flow signal erroneously generated from background), and the noise intensity in the foveal avascular zone (FAZ).

Results : Compared to the originals, the angiograms reconstructed by DCARnet significantly reduced noise intensity (ICP, 7.38±25.22, p<0.001; DCP, 11.20±22.52, p<0.001), improved vascular connectivity (ICP, 0.95±0.01, p<0.001; DCP, 0.96±0.01, p<0.001), and did not generate false flow signal at the level of noise intensity in normal FAZ. DCARnet not only enhanced the image quality of 6×6-mm ICP and DCP angiograms, but also reduced noise and improved connectivity in 3×3-mm ICP and DCP angiograms. Furthermore, DCARnet preserves the appearance of the dilated vessels in the reconstructed angiograms.

Conclusions : DCARnet can reconstruct high-resolution ICP and DCP angiograms from low-definition 6×6-mm en face OCTA images. The enhanced angiograms may improve characterization of biomarkers such as non-perfusion area and vessel density.

This is a 2021 ARVO Annual Meeting abstract.

 

 

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