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

Janek Gröhl; Thomas Kirchner; Tim Adler; Lena Maier-Hein

doi:10.1117/12.2545853

20 April 2020 Deep learning-based oxygenation estimation for multispectral photoacoustic imaging (Conference Presentation)

Janek Gröhl, Thomas Kirchner, Tim Adler, Lena Maier-Hein

Author Affiliations +

Proceedings Volume 11240, Photons Plus Ultrasound: Imaging and Sensing 2020; 112402P (2020) https://doi.org/10.1117/12.2545853
Event: SPIE BiOS, 2020, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Janek Gröhl, Thomas Kirchner, Tim Adler, and Lena Maier-Hein "Deep learning-based oxygenation estimation for multispectral photoacoustic imaging (Conference Presentation)", Proc. SPIE 11240, Photons Plus Ultrasound: Imaging and Sensing 2020, 112402P (20 April 2020); https://doi.org/10.1117/12.2545853

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