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

arXiv: https://arxiv.org/abs/2004.08957 · HTML: https://mlsanity.com/paper/2004.08957/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction](https://mlsanity.com/paper/2207.11882/) (2022)
- [Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN](https://mlsanity.com/paper/2309.17269/) (2023)
- [Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation](https://mlsanity.com/paper/2305.05835/) (2023)
- [Freqformer: Frequency-Domain Transformer for 3-D Reconstruction and Quantification of Human Retinal Vasculature](https://mlsanity.com/paper/2411.11189/) (2024)
- [Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning](https://mlsanity.com/paper/2301.00504/) (2023)
- [Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review](https://mlsanity.com/paper/2502.14935/) (2025)
- [Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography](https://mlsanity.com/paper/2606.05375/) (2026)

## Abstract (arXiv)

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.