Fig. 1

Schematic diagram illustrating the workflow of this study. The 39 prospectively collected eyes underwent 4 repeated 6 × 6 mm macular scans on the Zeiss PLEX Elite SS-OCT, and the repetitions were co-registered to generate an averaged volume. This dataset was split into a training dataset (5 eyes) and validation dataset (34 eyes). In the training dataset, the single-frame enface OCTA scan was used as an input and it was paired with its corresponding enface OCTA 4-frame average as a target to train the denoising or pseudoaveraging algorithm. Additional training of the algorithm (not shown here) was done by using 8 low-quality single-frame enface OCTA scans as an input and high-quality single-frame OCTA scan of the same eye as an output (peer-to-peer training). For the validation dataset, the pseudoaveraging algorithm was applied to the best-quality single-frame enface OCTA scan (out of the 4 repeated single-frame acquisitions), and the resulting pseudoaveraged scans were compared against their corresponding original single-frame scans as well as against their 4-frame averaged scans. In a separate test dataset of 105 eyes retrospectively collected, the algorithm was applied to single-frame enface OCTA scans, and the original and its corresponding pseudoaveraged enface scans were compared