Steady-State Model of the Radio-Pharmaceutical Uptake for MR-PET

This work explores a fully-automated algorithm for estimation of the uptake of radio-pharmaceutical in brain MR-PET imaging. The algorithm is based on a model of the pharmaceutical uptake coupled with probabilistic models of the PET and MR acquisition systems. In contrast to algorithms that attempt to correct for the partial volume effect (PVE), the problem is tackled here in the reconstruction by means of a probabilistic model of the pharmaceutical uptake. We make use of hybrid Bayesian networks to describe the joint probabilistic model and to obtain an efficient optimisation algorithm. We describe solutions adopted in order to mitigate the effect of local maxima and to reduce the sensitivity to the initialisation of the parameters, rendering the algorithm fully automatic. The algorithm is evaluated on simulated MR-PET data and on the reconstruction of clinical PET FDG acquisitions.