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5 March 2021 Reconstruction of tissue birefringence from polarization-sensitive optical coherence tomography using machine learning
Ludovick Bégin, Damon T. DePaoli, Brett E. Bouma, Daniel Côté, Martin Villiger
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Proceedings Volume 11646, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics; 1164612 (2021) https://doi.org/10.1117/12.2578448
Event: SPIE BiOS, 2021, Online Only
Abstract
Current processing techniques of polarization-sensitive optical coherence tomography (PS-OCT) can recover the tissue’s local, i.e. depth-resolved scalar retardance and optic axis orientation. However, system-induced polarization mode dispersion (PMD) and the presence of speckle in the measured tomograms complicate reconstruction and result in a detrimental trade-off with spatial resolution. We speculate that a machine learning approach should work well for generating an improved reconstruction. By training the model on simulated tomograms that encode the forward model and include system PMD and noise, and by testing the algorithm on experimentally acquired PS-OCT data, we aim to demonstrate a generalized PS-OCT reconstruction tool.
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Ludovick Bégin, Damon T. DePaoli, Brett E. Bouma, Daniel Côté, and Martin Villiger "Reconstruction of tissue birefringence from polarization-sensitive optical coherence tomography using machine learning", Proc. SPIE 11646, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics, 1164612 (5 March 2021); https://doi.org/10.1117/12.2578448
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KEYWORDS
Tissue optics
Birefringence
Optical coherence tomography
Network architectures
Machine learning
Polarization
Speckle
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