A Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic Positron Emission Tomography

Abstract

We present a fully Bayesian statistical approach to the problem of compartmental modelling in the context of Positron Emission Tomography. We cluster homogeneous region of interest and perform kinetic parameter estimation simultaneously. A mixture modelling approach is adopted, incorporating both spatial and temporal information based on reconstructed dynamic PET image. Our modelling approach is flexible, and provides uncertainty estimates for the estimated kinetic parameters. Crucially, the proposed method allows us to determine the unknown number of clusters, which has a great impact on resulting estimated kinetic parameters. We demonstrate our method on simulated dynamic Myocardial PET data, and show that our method is superior to standard curve-fitting approach.