Multiple illumination learned spectral decoloring for quantitative optoacoustic oximetry imaging

A method for accurate and applicable real-time quantification of local sO2 with OA imaging. Approach: We combine multiple illumination (MI) sensing with learned spectral decoloring (LSD). We train LSD feedforward neural networks and random forests on Monte Carlo simulations of spectrally colored absorbed energy spectra, to apply the trained models to real OA 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.

Random forest-based MI-LSD is a promising method for accurate quantitative OA oximetry imaging.

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 with LSD. Random forest regressors outperform previously reported neural network approaches. Conclusions: Random forest-based MI-LSD is a promising method for accurate quantitative OA oximetry imaging.

Quantitative measurement of blood oxygen saturation (sO2) with optoacoustic (OA) imaging is one of the most sought after goals of quantitative OA imaging research due to its wide range of biomedical applications. Aim: A method for accurate and applicable real-time quantification of local sO2 with OA imaging. Approach: We combine multiple illumination (MI) sensing with learned spectral decoloring (LSD). We train LSD feedforward neural networks and random forests on Monte Carlo simulations of spectrally colored absorbed energy spectra, to apply the trained models to real OA 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 with LSD. Random forest regressors outperform previously reported neural network approaches. Conclusions: Random forest-based MI-LSD is a promising method for accurate quantitative OA oximetry imaging.