Quantitative photoacoustic oximetry imaging by multiple illumination learned spectral decoloring
This work addresses the need for real-time, quantitative oximetry in biomedical imaging, though it appears incremental as it builds on existing methods with improvements in accuracy and outlier reduction.
The paper tackled the problem of accurately measuring blood oxygen saturation (sO2) in photoacoustic imaging by combining multiple illumination sensing with learned spectral decoloring, achieving median absolute estimation errors of 2.5 to 4.5 percentage points in phantom validation.
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.