Quantitative photoacoustic oximetry imaging by multiple illumination learned spectral decoloring

TL;DR

This paper introduces a novel method combining multiple illumination sensing with learned spectral decoloring for real-time, accurate quantification of blood oxygen saturation in photoacoustic imaging, validated on a sulfate phantom model.

Contribution

The study presents a new MI-LSD approach that improves accuracy and reduces outliers in photoacoustic oximetry, outperforming previous neural network methods.

Findings

Median absolute errors of 2.5 to 4.5 percentage points

Fewer outliers compared to LSD

Random forest regressors outperform neural networks

Abstract

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.