  Typical optical coherence tomographic angiography (OCTA) acquisition areas on
commercial devices are 3x3- or 6x6-mm. Compared to 3x3-mm angiograms with
proper sampling density, 6x6-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 6x6-mm superficial
vascular complex (SVC) angiograms. The network was trained on data from 3x3-mm
and 6x6-mm angiograms from the same eyes. The reconstructed 6x6-mm angiograms
have significantly lower noise intensity 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 6x6-mm OCTA.
