Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning

TL;DR

This paper presents a deep learning approach, based on an altered SRGAN, to reconstruct lost features in OCT images caused by reduced spectral bandwidth, aiming to enhance image resolution for better clinical decision-making.

Contribution

It introduces a novel deep learning method using an altered SRGAN architecture to recover spectral bandwidth in OCT images, improving resolution in wide-field systems.

Findings

Deep learning effectively reconstructs OCT features from reduced spectral data.

Altered SRGAN outperforms traditional super-resolution techniques.

Enhanced image resolution aids clinical diagnosis and decision-making.

Abstract

Optical coherence tomography (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 a 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. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.