【摘要】 运用基于行处理(RA)的"单块"投影子集法改进了空间交替广义期望最大(SAGE)算法的收敛性.新的RA-SAGE算法以正交单投影序列的方式对投影数据进行处理,以减少投影间的相关性,达到加速收敛的效果.此外,在迭代搜索同时,新算法结合了超松弛变量,使其能快速接近全局最大似然解.实验中,运用RA-SAGE与SAGE对正电子发射断层(PET)进行了重建.结果表明,RA-SAGE收敛性能比SAGE优越,且重建图像质量较高.更多还原

【Abstract】 The one-block version of ordered subsets (OS) techniques is used to accelerate the convergent rate of the space-alternating generalized expectation-maximization (SAGE) algorithm. The new row-action SAGE (RA-SAGE) algorithm processes projections in sequentially orthogonal order which reduce the dependency among the projections and speeds up the convergences. Additionally, the over-relaxation parameter in the direction defined by the RA-SAGE algorithm is also applied to obtain fast convergence to a globally maximum likelihood (ML) solution. In experiments, the RA-SAGE algorithm and the classical SAGE algorithm are compared in the application to positron emission tomography (PET) image reconstruction. Simulation results show that RA-SAGE has better performance than SAGE in both convergence and image quality.更多还原