Quantitative photoacoustic oximetry imaging by multiple illumination learned spectral decoloring
T Kirchner, M Frenz - arXiv preprint arXiv:2102.11201, 2021 - arxiv.org
arXiv preprint arXiv:2102.11201, 2021•arxiv.org
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 …
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 …
Significance
Quantitative measurement of blood oxygen saturation (sO) 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 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.
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