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.

利用深度学习和超分辨率技术重建失去的特征，以改善眼科临床医生的决策，应对广场视野 OCT 系统降低分辨率的挑战。