Reconstruction of visible light optical coherence tomography images retrieved from discontinuous spectral data using a conditional generative adversarial network.

ABSTRACT

Achieving high resolution in optical coherence tomography typically requires the continuous extension of the spectral bandwidth of the light source. This work demonstrates an alternative approach: combining two discrete spectral windows located in the visible spectrum with a trained conditional generative adversarial network (cGAN) to reconstruct a high-resolution image equivalent to that generated using a continuous spectral band. The cGAN was trained using OCT image pairs acquired with the continuous and discontinuous visible range spectra to learn the relation between low- and high-resolution data. The reconstruction performance was tested using 6000 B-scans of a layered phantom, micro-beads and ex-vivo mouse ear tissue. The resultant cGAN-generated images demonstrate an image quality and axial resolution which approaches that of the high-resolution system.