Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning

• URL: http://arxiv.org/abs/2301.00504v1
• Date: Mon, 2 Jan 2023 02:18:32 GMT
• Title: Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning
• Authors: Timothy T. Yu, Da Ma, Jayden Cole, Myeong Jin Ju, Mirza F. Beg and Marinko V. Sarunic
• Abstract summary: 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. 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.
• Score: 0.6990493129893112
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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