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Proceedings Paper

Deep learning-based oxygenation estimation for multispectral photoacoustic imaging (Conference Presentation)

Paper Abstract

One of the major applications of multispectral photoacoustic imaging is the recovery of functional tissue properties with the goal of distinguishing different tissue classes. In this work, we tackle this challenge by employing a deep learning-based algorithm called learned spectral decoloring for quantitative photoacoustic imaging. With the combination of tissue classification, sO2 estimation, and uncertainty quantification, powerful analyses and visualizations of multispectral photoacoustic images can be created. Consequently, these could be valuable tools for the clinical translation of photoacoustic imaging.

Paper Details

Date Published: 20 April 2020
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Proc. SPIE 11240, Photons Plus Ultrasound: Imaging and Sensing 2020, 112402P (20 April 2020); doi: 10.1117/12.2545853
Show Author Affiliations
Janek Gröhl, Deutsches Krebsforschungszentrum (Germany)
Thomas Kirchner, Deutsches Krebsforschungszentrum (Germany)
Tim Adler, Deutsches Krebsforschungszentrum (Germany)
Lena Maier-Hein, Deutsches Krebsforschungszentrum (Germany)


Published in SPIE Proceedings Vol. 11240:
Photons Plus Ultrasound: Imaging and Sensing 2020
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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