1. Image enhancement of wide-field retinal optical coherence tomography angiography by super-resolution angiogram reconstruction generative adversarial network

    Image enhancement of wide-field retinal optical coherence tomography angiography by super-resolution angiogram reconstruction generative adversarial network

    Wide-field retinal optical coherence tomography angiography (OCTA) usually suffers from low image resolution in clinical practice because of insufficient lateral sampling. In this study, we develop a deep-learning-based method named super-resolution angiogram reconstruction generative adversarial network (SAR-GAN) to enhance the en face OCTA image quality. A sophisticated home-made spectral-domain OCTA system is employed to capture the data of retinal angiograms with different scanning protocols. High-resolution 3 × 3 mm 2 OCTA images and low-resolution (LR) 6 × 6 mm 2 OCTA images are utilised in training the network. We propose an improved loss function for SAR-GAN for the reconstruction of perceptually enhanced ...

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