Spatio-temporal prior based kinetic model in dynamic PET reconstruction

Based on Markov Random Fields (MRF) theory, Bayesian approaches have been accepted as effective solutions to overcome the ill-posed problems of image restoration and reconstruction. Traditionally, the knowledge in most of prior models comes from simply weighted differences between the pixel intensities within a small local neighborhood, so it can only provide limited prior information for regularization. A novel dynamic image reconstruction method for PET is proposed which uses a spatio-temporal prior that constrains not only neighborhood information but also voxel´s behaviour in time to conform to 2-tissue compartmental model.