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

arXiv: https://arxiv.org/abs/1910.01344 · HTML: https://mlsanity.com/paper/1910.01344/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning](https://mlsanity.com/paper/2204.11769/) (2022)

## Abstract (arXiv)

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.