PET image reconstruction based on Bayesian inference regularised maximum likelihood expectation maximisation (MLEM) method

Abstract

A better quality of an image can be achieved through iterative image reconstruction for positron emission tomography (PET) as it employs spatial regularisation that minimises the difference of image intensity among adjacent pixels. In this paper, the Bayesian inference rule is applied to devise a novel approach to address the ill-posed inverse problem associated with the iterative maximum-likelihood Expectation-Maximisation (MLEM) algorithm by proposing a regularised constraint probability model. The proposed algorithm is more robust than the standard MLEM and in background noise removal with preserving edges to suppress the out of focus slice blur, which is the existent image artefact. The quality measurements and visual inspections show a significant improvement in image quality compared to conventional MLEM and the state-of-the-art regularised algorithms.