Markov Matrix-based Kernel Reconstruction Method for PET Images

Positron emission tomography (PET) is a powerful medical imaging technique. PET images obtained in clinical practice usually have low resolution. To address this problem, this paper develops the kernelized expectation maximization (KEM) algorithm and proposes Markov matrix-based kernel reconstruction method. The Markov matrix is constructed by the spatial prior information of PET data and applied to repair the data after each KEM iteration. The results of simulation experiments show that the proposed method has better performance in terms of evaluation metrics and visual effects compared to the classical maximum likelihood expectation maximization (MLEM) and some kernel-based reconstruction methods.