Blood Oxygenation Quantification in Multispectral Photoacoustic Tomography Using A Convex Cone Approach.

Abstract

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.