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We present a fully Bayesian statistical approach to the problem ofcompartmental modelling in the context of Positron Emission Tomography. Wecluster homogeneous region of interest and perform kinetic parameter estimationsimultaneously. A mixture modelling approach is adopted, incorporating bothspatial and temporal information based on reconstructed dynamic PET image. Ourmodelling approach is flexible, and provides uncertainty estimates for theestimated kinetic parameters. Crucially, the proposed method allows us todetermine the unknown number of clusters, which has a great impact on resultingestimated kinetic parameters. We demonstrate our method on simulated dynamicMyocardial PET data, and show that our method is superior to standardcurve-fitting approach.

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