Application of expectation maximization algorithms for image resolution improvement in a small animal PET system

Modern positron emission tomography (PET) systems, offering high counting rate capabilities, high sensitivity, and near-submillimeter coordinate resolution, require fast image reconstruction software that can operate on list-mode data and take into account most of finite resolution effects such as photon scattering, positron range in tissue, and detector features. It has already been demonstrated that the expectation maximization (EM) method with extended system matrix modeling looks very attractive for image resolution recovery in PET imaging studies. In this paper, the performance of EM-based algorithms (in particular, their ability to improve the image resolution) is evaluated for a small animal PET imager with several phantoms. The achievement of a substantial decrease in processing time using an EM deblurring procedure is shown, as is an approach to successfully treat what are essentially nonspace-invariant resolution effects within a shift-invariant model.