Inpainting of detector saturation artifacts in OCT with deep learning

Jonas Nienhaus; Philipp Matten; Thomas Schlegl; Hessam Roodaki; Wolfgang Drexler; Rainer A. Leitgeb; Tilman Schmoll

doi:10.1117/12.3005432

12 March 2024 Inpainting of detector saturation artifacts in OCT with deep learning

Jonas Nienhaus, Philipp Matten, Thomas Schlegl, Hessam Roodaki, Wolfgang Drexler, Rainer A. Leitgeb, Tilman Schmoll

Proceedings Volume 12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII; 128300Z (2024) https://doi.org/10.1117/12.3005432
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

In the presence of strong reflecting surfaces, the detector in SS-OCT may saturate, leading to loss of information within affected A-scans and potentially disturbing axial artifacts in affected B-scans or volumes. In this work, we trained an image-based neural network to detect and remove such artifacts and restore the underlying structure by means of image inpainting. For this purpose, sets of paired images were generated from raw OCT spectra, with one image intact and the other suffering from simulated detector saturation. We demonstrate the effectiveness of the proposed method qualitatively and quantitatively.

Conference Presentation

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

Citation Download Citation

Jonas Nienhaus, Philipp Matten, Thomas Schlegl, Hessam Roodaki, Wolfgang Drexler, Rainer A. Leitgeb, and Tilman Schmoll "Inpainting of detector saturation artifacts in OCT with deep learning", Proc. SPIE 12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII, 128300Z (12 March 2024); https://doi.org/10.1117/12.3005432

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