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19 January 2025 |
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Article overview
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Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning | | Timothy T. Yu
; Da Ma
; Jayden Cole
; Myeong Jin Ju
; Mirza F. Beg
; Marinko V. Sarunic
; | | Date: |
2 Jan 2023 | | 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. | | Source: | arXiv, 2301.00504 | | Services: | Forum | Review | PDF | Favorites | |
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