Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning
This work addresses a domain-specific problem for clinical ophthalmology by improving image quality for biomarker measurements and assessment, though it is incremental as it builds on existing deep learning methods applied to known undersampling issues in OCTA.
The authors tackled the problem of low-quality 6x6-mm optical coherence tomographic angiography (OCTA) images by proposing HARNet, a deep-learning-based reconstruction network, which resulted in significantly lower noise intensity and better vascular connectivity in the enhanced images without generating false flow signals.
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.