Dispersion-contrast imaging using machine learning

Krzysztof A. Maliszewski; Varvara Vetrova; Heyang (Thomas) Li; Piotr Kolenderski; Sylwia M. Kolenderska

doi:10.1117/12.2612671

7 March 2022 Dispersion-contrast imaging using machine learning

Krzysztof A. Maliszewski, Varvara Vetrova, Heyang (Thomas) Li, Piotr Kolenderski, Sylwia M. Kolenderska

Proceedings Volume 11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI; 119480Q (2022) https://doi.org/10.1117/12.2612671
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

In Intensity Correlation Optical Coherence Tomography (ICA-OCT), an OCT spectrum is processed into a two-dimensional signal incorporating elements which do not correspond to the structure of the imaged object. These elements, called artefacts, display a very well-defined behaviour in the presence of uncompensated chromatic dispersion. More importantly, their behaviour reflects only the dispersion of the layer which the artefacts uniquely correspond to. We show preliminary results indicating that a neural network can interpret this layer-specific behaviour and output corresponding Group Velocity Dispersion values, thus creating a depth-resolved dispersion profile of the object.

Conference Presentation

Citation Download Citation

Krzysztof A. Maliszewski, Varvara Vetrova, Heyang (Thomas) Li, Piotr Kolenderski, and Sylwia M. Kolenderska "Dispersion-contrast imaging using machine learning", Proc. SPIE 11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, 119480Q (7 March 2022); https://doi.org/10.1117/12.2612671

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