Self super-resolution of optical coherence tomography images based on deep learning

Abstract

As a medical imaging modality, many researches have been devoted to improving the resolution of optical coherence tomography (OCT). We developed a deep-learning based OCT self super-resolution (OCT-SSR) pipeline to improve the axial resolution of OCT images based on the high-resolution and low-resolution spectral data collected by the OCT system. In this pipeline, the enhanced super-resolution asymmetric generative adversarial networks were built to improve the network outputs without increasing the complexity. The feasibility and effectiveness of the approach were demonstrated by experimental results on the images of the biological samples collected by the home-made spectral-domain OCT and swept-source OCT systems. More importantly, we found the sidelobes in the original images can be obviously suppressed while improving the resolution based on the OCT-SSR method, which can help to reduce pseudo-signal in OCT imaging when non-Gaussian spectra light source is used. We believe that the OCT-SSR method has broad prospects in breaking the limitation of the source bandwidth on the axial resolution of the OCT system.