Robust inference of baseline optical properties of the human head with three-dimensional segmentation from magnetic resonance imaging

Abstract

We model the capability of a small (6-optode) time-resolved diffuse optical tomography (DOT) system to infer baseline absorption and reduced scattering coefficients of the tissues of the human head (scalp, skull, and brain). Our heterogeneous three-dimensional diffusion forward model uses tissue geometry from segmented magnetic resonance (MR) data. Handling the inverse problem by use of Bayesian inference and introducing a realistic noise model, we predict coefficient error bars in terms of detected photon number and assumed model error. We demonstrate the large improvement that a MR-segmented model can provide: 2-10% error in brain coefficients (for 2 x 10(6) photons, 5% model error). We sample from the exact posterior and show robustness to numerical model error. This opens up the possibility of simultaneous DOT and MR for quantitative cortically constrained functional neuroimaging.