  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.
