BACKGROUND:A statistical method called maximum likelihood expectation maximization (MLEM) is quite attractive, especially in PET/SPECT. However, the convergence rate of the iterative scheme of MLEM is quite slow. OBJECTIVE:This study aims to develop and test a new method to speed up the convergencerate of the MLEM algorithm. METHODS:We introduce a relaxation parameter in the conventional MLEM iterative formula and propose the relaxation strategy on the condition that the spectral radius of the derived iterative matrix from the iterative scheme with the accelerated parameter reaches a minimum value. RESULTS:Experiments with Shepp-Logan phantom and an annual tree image demonstrate that the new computational strategy effectively accelerates computation time while maintains reasonable image quality. CONCLUSIONS:The proposed new computational method involving the relaxation strategy has a faster convergence speed than the original method.

中文翻译：

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MLEM 算法的加速策略

背景：一种称为最大似然期望最大化（MLEM）的统计方法非常有吸引力，尤其是在PET/SPECT中。然而，MLEM 的迭代方案的收敛速度相当慢。目的：本研究旨在开发和测试一种新方法，以加快 MLEM 算法的收敛速度。方法：在常规MLEM迭代公式中引入松弛参数，并在加速参数迭代方案推导出的迭代矩阵谱半径达到最小值的条件下提出松弛策略。结果：Shepp-Logan 体模和一年生树图像的实验表明，新的计算策略有效地加快了计算时间，同时保持了合理的图像质量。结论：