Open AccessReconstruction of visible light optical coherence tomography images retrieved from discontinuous spectral data using a conditional generative adversarial network
Author(s) -
Antonia Lichtenegger,
Matthias Salas,
Alexander Sing,
M. Duelk,
Roxane Licandro,
Johanna Gesperger,
Bernhard Baumann,
Wolfgang Drexler,
Rainer A. Leitgeb
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.435124
Subject(s) - optical coherence tomography , optics , computer science , image quality , artificial intelligence , iterative reconstruction , generative adversarial network , spectral imaging , imaging phantom , coherence (philosophical gambling strategy) , visible spectrum , computer vision , physics , image (mathematics) , quantum mechanics
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.