Improved image quality in dynamic OCT imaging by reduced imaging time and machine learning based data evaluation

Noah Heldt; Cornelia Holzhausen; Martin Ahrens; Mario Pieper; Peter König; Gereon Hüttmann

doi:10.1117/12.3005413

13 March 2024 Improved image quality in dynamic OCT imaging by reduced imaging time and machine learning based data evaluation

Noah Heldt, Cornelia Holzhausen, Martin Ahrens, Mario Pieper, Peter König, Gereon Hüttmann

Author Affiliations +

Proceedings Volume PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII; PC128302A (2024) https://doi.org/10.1117/12.3005413
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Dynamic Optical Coherence Tomography combines high resolution tomographic imagery with a cell specific contrast by Fourier analysis. However, the conversion from frequency space into RGB images by binning requires a priori knowledge and artifacts due to global movements provide another obstacle for in vivo application. We could show that an automated binning based on the Neural Gas algorithm can yield the highest spectral contrast without a priori knowledge and that motion artifacts can be reduced with shorter sequence lengths. Imaging murine airways, we observed that even just 6 frames are enough to generate dOCT images without losing important image information.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Noah Heldt, Cornelia Holzhausen, Martin Ahrens, Mario Pieper, Peter König, and Gereon Hüttmann "Improved image quality in dynamic OCT imaging by reduced imaging time and machine learning based data evaluation", Proc. SPIE PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII, PC128302A (13 March 2024); https://doi.org/10.1117/12.3005413

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