Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning

Abstract

Optical coherence tomography (OCT) is a noninvasive imaging modality utilized by ophthalmologists to acquire volumetric data to characterize the retina, the light-sensitive tissue at the back of the eye. OCT captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. Our experiment is limited by the size of our current dataset, and we leverage techniques like transfer learning from large natural image databases and image augmentation in our implementation. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we attempt to reconstruct lost features using a pixel-to-pixel approach with an altered super-resolution GAN (SRGAN) architecture. Similar techniques have been used to upscale images of lower image size and resolution in medical images like radiographs. We build upon methods of super-resolution to explore methods of better aiding clinicians in their decision-making to improve patient outcomes.