Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning

TL;DR

This paper introduces a deep learning method using cycle-consistent adversarial networks to enhance the resolution of OCTA images affected by low transverse sampling, improving image quality and clinical utility.

Contribution

It presents a novel deep learning approach for resolution enhancement in OCTA, demonstrating improved image quality and clinical applicability over traditional methods.

Findings

Generated images are closer to native high-resolution scans.

Enhanced signal-to-noise ratio in processed images.

Method generalizes well to diseased cases.

Abstract

Optical coherence tomography angiography (OCTA) requires high transverse sampling density for visualizing retinal and choroidal capillaries. Low transverse sampling causes 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 x 3 mm2 field of view (FOV) of the 8 x 8 mm2 foveal OCTA images (a sampling density of 22.9 $\mu$m) to the native 3 x 3 mm2 en face OCTA images (a sampling density of 12.2 $\mu$m). 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 x 3 mm2 scans. Besides, the results show the proposed method could also enhance 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.