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8 March 2023 Real-time neural-network-based denoising for intraoperative 4D-OCT
Jonas Nienhaus, Philipp Matten, Anja Britten, Thomas Schlegl, Eva Höck, Alexander Freytag, Matt Everett, Nancy Hecker-Denschlag, Wolfgang Drexler, Rainer A. Leitgeb, Tilman Schmoll
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Proceedings Volume 12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII; 123670T (2023) https://doi.org/10.1117/12.2652855
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
Noise decreases image quality in optical coherence tomography (OCT) and can obscure important features in real-time visualizations. In this work, we show that a neural network can be applied to denoise volumetric OCT data for intra-surgical visualization in real-time. We adapt a self-supervised training approach, not requiring any paired data for training. Several optimizations and trade-offs in deployment are required, with which we achieved processing times of only few milliseconds. While still being limited by the real-time requirements, denoising in this scenario can enhance surface visibility, and therefore allow guidance for more precise intra-surgical maneuvers.
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Jonas Nienhaus, Philipp Matten, Anja Britten, Thomas Schlegl, Eva Höck, Alexander Freytag, Matt Everett, Nancy Hecker-Denschlag, Wolfgang Drexler, Rainer A. Leitgeb, and Tilman Schmoll "Real-time neural-network-based denoising for intraoperative 4D-OCT", Proc. SPIE 12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII, 123670T (8 March 2023); https://doi.org/10.1117/12.2652855
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KEYWORDS
Denoising
Optical coherence tomography
Artificial neural networks
Neural networks
Signal to noise ratio
GPU based image processing
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