Artefact removal for quantum optical coherence tomography using machine learning

Krzysztof A. Maliszewski; Sylwia M. Kolenderska

doi:10.1117/12.2583540

5 March 2021 Artefact removal for quantum optical coherence tomography using machine learning

Krzysztof A. Maliszewski, Sylwia M. Kolenderska

Proceedings Volume 11630, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV; 1163012 (2021) https://doi.org/10.1117/12.2583540
Event: SPIE BiOS, 2021, Online Only

Abstract

Quantum Optical Coherence Tomography (Q-OCT) is a non-classical equivalent of Optical Coherence Tomog- raphy (OCT) able to provide an increased axial resolution and immunity to even orders of dispersion. The main drawback of Q-OCT is artefacts which are additional elements that clutter an A-scan and lead to a complete loss of structural information for multilayered objects. To retrieve the structure of the object, we propose to use machine learning in Fourier domain Q-OCT. We present preliminary results for a model based on VGG16 architecture and compare it to analytical algorithms. We show on computer-generated data that machine learning outperforms previously proposed analytical algorithms. The trained neural network requires much less input data and achieves much better results than the analytical algorithms. Finally, due to the way the training of the neural network is performed and the increased axial resolution of Q-OCT, machine learning provides super-resolution - it is able to distinguish two peaks which are otherwise unresolved in traditional OCT.

Conference Presentation

Citation Download Citation

Krzysztof A. Maliszewski and Sylwia M. Kolenderska "Artefact removal for quantum optical coherence tomography using machine learning", Proc. SPIE 11630, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV, 1163012 (5 March 2021); https://doi.org/10.1117/12.2583540

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