Application of SAGE algorithm in PET image reconstruction using modified ordered subsets

摘要

A new method that uses a modified ordered subsets (MOS) algorithm to improve the convergence rate of space-alternating generalized expectation-maximization ( SAGE) algorithm for positron emission tomography (PET) image reconstruction is proposed. In the MGS-SAGE algorithm, the number of projections and the access order of the subsets are modified in order to improve the quality of the reconstructed images and accelerate the convergence speed. The number of projections in a subset increases as follows: 2,4,8,16, 32 and 64. This sequence means that the high frequency component is recovered first and the low frequency component is recovered in the succeeding iteration steps. In addition, the neighboring subsets are separated as much as possible so that the correlation of projections can be decreased and the convergences can be speeded up. The application of the proposed method to simulated and real images shows that the MOS-SAGE algorithm has better performance than the SAGE algorithm and the OSEM algorithm in convergence and image quality.