Machine learning enabled multiple illumination quantitative optoacoustic oximetry imaging in humans.

ABSTRACT: Optoacoustic (OA) imaging is a promising modality for quantifying blood oxygen saturation (sO₂) in various biomedical applications - in diagnosis, monitoring of organ function, or even tumor treatment planning. We present an accurate and practically feasible real-time capable method for quantitative imaging of sO₂ based on combining multispectral (MS) and multiple illumination (MI) OA imaging with learned spectral decoloring (LSD). For this purpose we developed a hybrid real-time MI MS OA imaging setup with ultrasound (US) imaging capability; we trained gradient boosting machines on MI spectrally colored absorbed energy spectra generated by generic Monte Carlo simulations and used the trained models to estimate sO₂ on real OA measurements. We validated MI-L

SUBMITTER: Kirchner T 

PROVIDER: S-EPMC9203099 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature