Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning

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 6ÃŮ6-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. 1. Introduction Optical coherence tomographic angiography (OCTA) is a noninvasive imaging technology that can capture retinal and choroidal microvasculature invivo [1]. Clinicians are rapidly adopting OCTA for evaluation of various diseases, including diabetic retinopathy (DR) [2, 3], age-related macular degeneration (AMD) [4, 5], glaucoma [6, 7], and retinal vessel occlusion (RVO) [8, 9].High-resolution and large-field-of-view OCTA improve clinical observations, provide useful biomarkers and enhance the understanding of retinal and choroidal microvascular circulations [10-13].