基于改进的有序子集的SAGE算法在PET图像重建中的应用

提出了一种利用修改的有序子集(MOS)方法改进空间交替广义期望最大(SAGE)算法收敛性的方法.新的可变有序子集算法(MOS-SAGE)通过修改投影数据的数目和子集的排列循序加速收敛速度.其中每一个子集中的投影数目按2,4,8,16,32,64来排列以便重建算法首先恢复高频部分信息,然后重建低频部分信息.另外新算法还使相邻子集尽可能分离以减少投影间的相关性,达到加速收敛的效果.实验中,运用MOS-SAGE算法对计算机仿真的PET投影数据和实际的临床数据进行重建.几种误差分析结果表明,MOS-SAGE算法的收敛性能比SAGE算法和有序子集期望最大算法(OSEM)要快,重建后的图像更接近仿真用的模板图像.

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

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 MOS-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.