Reconstruction of high-resolution 6×6-mm OCT angiograms using deep learning

MIN GAO, YUKUN GUO, TRISTAN T. HORMEL, JIANDE SUN, THOMAS S. HWANG, YALI JIA

Research output: Contribution to journalArticlepeer-review

Abstract

Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3×3- or 6×6-mm. Compared to 3×3-mm angiograms with proper sampling density, 6×6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6×6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3×3-mm and 6×6-mm angiograms from the same eyes. The reconstructed 6×6- mm angiograms have significantly lower noise intensity, stronger contrast and better vascular connectivity than the original images. The algorithm did not generate false flow signal at the noise level presented by the original angiograms. The image enhancement produced by our algorithm may improve biomarker measurements and qualitative clinical assessment of 6×6-mm OCTA.

Original languageEnglish (US)
Pages (from-to)3585-3600
Number of pages16
JournalBiomedical Optics Express
Volume11
Issue number7
DOIs
StatePublished - Jul 1 2020

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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