Artificial intelligence-assisted projection-resolved optical coherence tomographic angiography (aiPR-OCTA)

We improved voxel-wise projection-resolved optical coherence tomographic angiography (PR-OCTA) using artificial intelligence. For generating a high-quality ground truth, our approach involved graders editing the flow signal to achieve an optimal appearance in the inner/outer retina and choroid through a rule-based PR-OCTA algorithm, ensuring the preservation of in situ flow signals (ground truth) while removing residual artifacts. The developed model employs a convolutional neural network to generate projection-resolved OCTA volumes from structural OCT and OCTA inputs. We evaluated the artificial intelligence PR-OCTA (aiPR-OCTA) algorithm on 126 normal eyes by assessing structural similarity (SSIM), flow signal-to-noise ratio (fSNR), and residual artifact strength. Compared to the existing state-of-the-art rule-based PR-OCTA algorithm, aiPR-OCTA demonstrated superior artifact removal, better preservation of flow signals, and accurate maintenance of anatomical details at the capillary scale. Additionally, it achieved a higher fSNR and reduced background artifacts.