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

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.