Blood Oxygenation Quantification in Multispectral Photoacoustic Tomography Using a Convex Cone Approach

Multispectral photoacoustic tomography (PAT) can create high spatial and temporal resolution images of oxygen saturation (sO2) distribution in deep tissue. However, unknown distributions of photon absorption and scattering introduces complex modulations to the photoacoustic (PA) spectra, dramatically reducing the accuracy of sO2 quantification. In this study, a rigorous light transport model was employed to unveil that the PA spectra corresponding to distinct sO2 values can be constrained within separate convex cones (CCs). Based on the CC model, sO2 estimation is achieved by identifying the CC nearest to the measured data through a modified Gilbert-Johnson-Keerthi (GJK) algorithm. The CC method combines a rigorous physical model with data-driven approach, and shows outstanding robustness in numerical, phantom, and in vivo imaging experiments validated against ground truth measurements. The average sO2 estimation error is approximately only 3% in in vivo human experiments, underscoring its potential for clinical application. All of our computer codes and data are publicly available on GitHub.