  Significance: Quantitative measurement of blood oxygen saturation (sO$_2$)
with photoacoustic (PA) imaging is one of the most sought after goals of
quantitative PA imaging research due to its wide range of biomedical
applications.
  Aim: A method for accurate and applicable real-time quantification of local
sO$_2$ with PA imaging.
  Approach: We combine multiple illumination (MI) sensing with learned spectral
decoloring (LSD); training on Monte Carlo simulations of spectrally colored
absorbed energy spectra, in order to apply the trained models to real PA
measurements. We validate our combined MI-LSD method on a highly reliable,
reproducible and easily scalable phantom model, based on copper and nickel
sulfate solutions.
  Results: With this sulfate model we see a consistently high estimation
accuracy using MI-LSD, with median absolute estimation errors of 2.5 to 4.5
percentage points. We further find fewer outliers in MI-LSD estimates compared
to LSD. Random forest regressors outperform previously reported neural network
approaches. Conclusions: Random forest based MI-LSD is a promising method for
accurate quantitative PA oximetry imaging.

Medical Physics;Machine Learning;Image and Video Processing