PET image reconstruction based on particle filter framework

PET measured data in nature follows Poisson distribution, which leads to iterative statistical methods being the primary efforts in image reconstruction. In contrast, physiological model provides a predictive tool of the imaged biological processes. To date, most of the existing efforts do not attempt to tackle the reconstruction problem by combining measured data statistics and physiological modeling constraints in a joint fashion. In this paper, we propose a novel approach that combine the statistical model and physiological parameter together during static reconstruction with the aid of particle filter. Experiments on Monte Carlo simulations, real physical phantom data demonstrate the power of the framework.