Bridging the gap: Using deep learning to reconstruct noise-reduced super-resolved OCT images from gapped spectra

Abstract

Fourier-domain (FD) optical coherence tomography (OCT) depends on broadband sources to maximize axial resolution and image quality. However, these lasers significantly drive device cost or may be unavailable at desired wavelength and bandwidth ranges. A potential solution lies in integrating multiple, more affordable sources with lower individual bandwidth into a single system. However, difficulties arise if the resulting spectrum exhibits discontinuities. In this letter, we present a method that can combine OCT images from a flexible number of spectra with arbitrary, possibly non-overlapping gaps using a neural network. Compared to low-resolution input images, reconstructed B-scans are super-resolved, preserve even fine and low-contrast details and edges, and exhibit strong noise reduction that increases with the band gap. The proposed method could thereby provide a major step towards high-quality OCT imaging using spectrally disjoint, low-bandwidth sources. In broadband settings, it can be directly applied as a Fourier-domain masked autoencoder for self-supervised image quality enhancement.