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 μm) to the native 3 x 3 mm2 en face OCTA images (a sampling density of 12.2 μm). 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.
@article{arxiv.1910.01344,
title = {Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning},
author = {Ting Zhou and Kang Zhou and Jianlong Yang and Liyang Fang and Yan Hu and Yitian Zhao and Jun Cheng and Xiangping Chen and Shenghua Gao and Jiang Liu},
journal= {arXiv preprint arXiv:1910.01344},
year = {2020}
}