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- W4313484195 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." @default.
- W4313484195 created "2023-01-06" @default.
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- W4313484195 date "2023-01-01" @default.
- W4313484195 modified "2023-09-27" @default.
- W4313484195 title "Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning" @default.
- W4313484195 doi "https://doi.org/10.48550/arxiv.2301.00504" @default.
- W4313484195 hasPublicationYear "2023" @default.
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