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

TL;DR

This paper introduces a Bayesian mixture model for dynamic PET imaging that clusters regions, estimates kinetic parameters with uncertainty, and adaptively determines the number of clusters, outperforming standard methods.

Contribution

It presents a novel Bayesian spatial-temporal mixture model for kinetic parameter estimation in dynamic PET, with automatic cluster number determination and uncertainty quantification.

Findings

Outperforms standard curve-fitting methods on simulated data

Provides uncertainty estimates for kinetic parameters

Automatically determines the number of clusters

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.