Abstract
Purpose :
We propose to use deep learning to reconstruct flow signal from under-sampled 6×6-mm optical coherence tomographic angiography (OCTA) images of the intermediate capillary plexus (ICP) and deep capillary plexus (DCP).
Methods :
6×6-mm macular scans with a 400×400 A-line sampling density and 3×3-mm scans with a 304×304 A-line sampling density were acquired on one or both eyes of 180 participants (including 230 eyes with diabetic retinopathy (DR) and 44 healthy controls) using a 70-kHz commercial OCT system (RTVue-XR; Optovue, Inc.). Projection-resolved OCTA algorithm was applied to remove projection artifacts in voxel. ICP and DCP angiograms were generated by maximum projection of the OCTA signal within the relevant plexus. We proposed a deep-learning-based method, dubbed “deep capillary angiograms reconstruction network” (DCARnet), to reconstruct 6×6-mm high-resolution ICP and DCP en face OCTA images from sparsely-sampled, low-resolution scans of the same area. DCARnet takes registered 3×3-mm ICP and DCP angiograms with proper sampling density as the ground truth reference. Same network can also be applied on 3×3-mm angiograms. We evaluated the reconstructed 3×3- and 6×6-mm angiograms based on vessel connectivity, false flow signal (flow signal erroneously generated from background), and the noise intensity in the foveal avascular zone (FAZ).
Results :
Compared to the originals, the angiograms reconstructed by DCARnet significantly reduced noise intensity (ICP, 7.38±25.22, p<0.001; DCP, 11.20±22.52, p<0.001), improved vascular connectivity (ICP, 0.95±0.01, p<0.001; DCP, 0.96±0.01, p<0.001), and did not generate false flow signal at the level of noise intensity in normal FAZ. DCARnet not only enhanced the image quality of 6×6-mm ICP and DCP angiograms, but also reduced noise and improved connectivity in 3×3-mm ICP and DCP angiograms. Furthermore, DCARnet preserves the appearance of the dilated vessels in the reconstructed angiograms.
Conclusions :
DCARnet can reconstruct high-resolution ICP and DCP angiograms from low-definition 6×6-mm en face OCTA images. The enhanced angiograms may improve characterization of biomarkers such as non-perfusion area and vessel density.
This is a 2021 ARVO Annual Meeting abstract.