Significance: 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.

中文翻译：

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用于定量光声血氧饱和度成像的多重照明学习光谱脱色

意义：由于其广泛的生物医学应用，使用光声 (OA) 成像定量测量血氧饱和度 (sO2) 是定量 OA 成像研究最受追捧的目标之一。目的：一种通过 OA 成像准确、适用地实时量化局部 sO2 的方法。方法：我们将多重照明 (MI) 传感与学习光谱脱色 (LSD) 相结合。我们在光谱彩色吸收能谱的蒙特卡罗模拟上训练 LSD 前馈神经网络和随机森林，以将训练后的模型应用于实际的 OA 测量。我们在基于硫酸铜和硫酸镍溶液的高度可靠、可重现且易于扩展的体模模型上验证了我们的组合 MI-LSD 方法。结果：使用这个硫酸盐模型，我们看到使用 MI-LSD 的估计精度始终很高，中值绝对估计误差为 2.5 至 4.5 个百分点。与 LSD 相比，我们进一步发现 MI-LSD 估计中的异常值更少。随机森林回归器的性能优于之前报道的神经网络方法。结论：基于随机森林的 MI-LSD 是一种用于准确定量 OA 血氧饱和度成像的有前途的方法。