Frequency-Aware Inverse-Consistent Deep Learning for OCT-Angiogram Super-Resolution

Optical Coherence Tomography Angiography (OCTA) is a novel imaging modality that captures the retinal and choroidal microvasculature in a non-invasive way. So far, 3 mm $$\times $$ 3 mm and 6 mm $$\times $$ 6 mm scanning protocols have been the two most widely-used field-of-views. Nevertheless, since both are acquired with the same number of A-scans, resolution of 6 mm $$\times $$ 6 mm image is inadequately sampled, compared with 3 mm $$\times $$ 3 mm. Moreover, conventional supervised super-resolution methods for OCTA images are trained with pixel-wise registered data, while clinical data is mostly unpaired. This paper proposes an inverse-consistent generative adversarial network (GAN) for archiving 6 mm $$\times $$ 6 mm OCTA images with super-resolution. Our method is designed to be trained with unpaired 3 mm $$\times $$ 3 mm and 6 mm $$\times $$ 6 mm OCTA image datasets. To further enhance the super-resolution performance, we introduce frequency transformations to refine high-frequency information while retaining low-frequency information. Compared with other state-of-the-art methods, our approach outperforms them on various performance metrics.