ArticleJournal of biomedical optics2021
Multiple illumination learned spectral decoloring for quantitative optoacoustic oximetry imaging.
Article in Journal of biomedical optics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Blood-mimicking dye phantoms for evaluating photoacoustic oximetry accuracy.Photoacoustics · 2026Article
- Anthropomorphic tissue-mimicking phantoms for oximetry validation in multispectral optical imaging.Journal of biomedical optics · 2025Article
- Automatic photoacoustic monitoring of perinatal brain hypoxia with superior sagittal sinus detection.Journal of biomedical optics · 2025Article
- Optimizing quantitative photoacoustic imaging systems: the Bayesian Cramér-Rao bound approach.Inverse problems · 2024Article
- Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry.Journal of biomedical optics · 2024Article
- Quantitative photoacoustic tomography: modeling and inverse problems.Journal of biomedical optics · 2024Review
- Validation of photoacoustic/ultrasound dual imaging in evaluating blood oxygen saturation.Biomedical optics express · 2022Article
- Machine learning enabled multiple illumination quantitative optoacoustic oximetry imaging in humans.Biomedical optics express · 2022Article
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Authors and funding
2 authors.
Funding
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Abstract
significanceQuantitative 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.
aimA 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.
resultsWith 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.
conclusionsRandom forest-based MI-LSD is a promising method for accurate quantitative OA oximetry imaging.
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