Abstract
Positron Emission Tomography (PET) is a powerful imaging tool which can provide physiological information using molecular tracers. The main advantages of statistical iterative reconstruction algorithm can apply Poisson modeling to describe coincident photon pairs, and permit the inclusion of many physical factors to reduce unfavorable artifacts. The thesis of this work is to investigate various geometric models and their influence on algorithm convergence of statistical image reconstruction. We consider three geometric models: interpolative, area-based and solid-angle. The iterative algorithm to evaluate the convergence performance is the Maximum Likelihood Expectation and Maximization (MLEM) algorithm. From the plot of log-likelihood curves, the sold-angle model can reach the highest value at early iterations. It means that the MLEM algorithm with solid-angle model will converge faster than the other models. In addition, the image results generated by solid-angle model exhibit better contrast recovery. Therefore, the solid-angle model is a favorable geometric model for iterative PET image reconstruction.