A PML algorithm for positron emission tomography based on Poisson-modified total variation model

Abstract

Recently, positron emission tomography (PET) has been widely used in medical image reconstruction. However, because of low tracer dosages and other reasons, the PET images are usually strongly polluted by noise, especially Poisson noise. The results of clinical diagnosis will be seriously affected by this noise.In order to suppress Poisson noise in reconstructed images, a new penalized maximum likelihood algorithm is proposed in this paper. It combines the Poisson-modified total variation model with the maximum likelihood expectation-maximization (MLEM) algorithm. Iterations of the proposed method can be divided into two steps: firstly, reconstructing image with the MLEM algorithm; secondly, suppressing Poison noise with the Poisson-modified total variation model. Experimental results demonstrate that the proposed method can effectively suppresses Poison noise in the PET images and is superior to many existing excellent algorithms.