Invertible neural networks for uncertainty quantification in photoacoustic imaging

Jan-Hinrich Nölke; Tim J. Adler; Janek Gröhl; Lena Maier-Hein; Thomas Kirchner; Lynton Ardizzone; Carsten Rother; Ullrich Köthe

doi:10.1117/12.2578183

5 March 2021 Invertible neural networks for uncertainty quantification in photoacoustic imaging

Jan-Hinrich Nölke, Tim J. Adler, Janek Gröhl, Lena Maier-Hein, Thomas Kirchner, Lynton Ardizzone, Carsten Rother, Ullrich Köthe

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Proceedings Volume 11642, Photons Plus Ultrasound: Imaging and Sensing 2021; 116421Q (2021) https://doi.org/10.1117/12.2578183
Event: SPIE BiOS, 2021, Online Only

Abstract

Photoacoustics Imaging is an emerging imaging modality enabling the recovery of functional tissue parameters such as blood oxygenation. However, quantifying these still remains challenging mainly due to the non-linear influence of the light fluence which makes the underlying inverse problem ill-posed. We tackle this gap with invertible neural networks and present a novel approach to quantifying uncertainties related to reconstructing physiological parameters, such as oxygenation. According to in silico experiments, blood oxygenation prediction with invertible neural networks combined with an interactive visualization could serve as a powerful method to investigate the effect of spectral coloring on blood oxygenation prediction tasks.

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Jan-Hinrich Nölke, Tim J. Adler, Janek Gröhl, Lena Maier-Hein, Thomas Kirchner, Lynton Ardizzone, Carsten Rother, and Ullrich Köthe "Invertible neural networks for uncertainty quantification in photoacoustic imaging", Proc. SPIE 11642, Photons Plus Ultrasound: Imaging and Sensing 2021, 116421Q (5 March 2021); https://doi.org/10.1117/12.2578183

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