【摘要】 提出了一种可调子块迭代(RBI)方法加速空间交替广义期望最大(SAGE)算法的收敛性.新的可调子块迭代的空间交替广义期望最大算法(RBI SAGE)组合了RBI算法和SAGE算法的优点用于加速正电子发射断层(PET)图像重建.RBI SAGE将投影数据分成不连续的子块,每一次迭代仅包含一个这样的子块.在每一个子块中用SAGE算法序列更新参数.实验中,运用RBI SAGE算法与SAGE算法对PET图像进行重建.结果表明,RBI SAGE收敛性能比SAGE算法优越,且重建图像质量较高.更多还原

【Abstract】 A new method to accelerate the convergent rate of the space-alternating generalized expectation-maximization (SAGE) algorithm is proposed.The new rescaled block-iterative SAGE (RBI-SAGE) algorithm combines the RBI algorithm with the SAGE algorithm for PET image reconstruction.In the new approach,the projection data is partitioned into disjoint blocks;each iteration step involves only one of these blocks.SAGE updates the parameters sequentially in each block.In experiments,the RBI-SAGE algorithm and classical SAGE algorithm are compared in the application on positron emission tomography (PET) image reconstruction.Simulation results show that RBI-SAGE has better performance than SAGE in both convergence and image quality.更多还原