一种用于期望最大化最大似然（EM-ML）正电子发射断层扫描（PET）重建的注意力焦点预处理方案。

A focus-of-attention preprocessing scheme for EM-ML PET reconstruction.

作者信息

Gregor J, Huff D A

出版信息

IEEE Trans Med Imaging. 1997 Apr;16(2):218-23. doi: 10.1109/42.563667.

Abstract

The expectation-maximization maximum-likelihood (EM-ML) algorithm belongs to a family of algorithms that compute positron emission tomography (PET) reconstructions by iteratively solving a large linear system of equations. We describe a preprocessing scheme for automatically focusing the attention, and thus the computational resources, on a subset of the equations and unknowns. Experimental work with a CM-5 parallel computer implementation using a simulated phantom as well as real data obtained from an ECAT 921 PET scanner indicates that quite significant savings can be obtained with respect to both time and space requirements of the EM-ML algorithm without compromising the quality of the reconstructed images.

摘要

期望最大化最大似然（EM-ML）算法属于一类通过迭代求解大型线性方程组来计算正电子发射断层扫描（PET）重建的算法。我们描述了一种预处理方案，用于自动将注意力，进而将计算资源，集中在方程组和未知数的一个子集上。使用模拟体模以及从ECAT 921 PET扫描仪获得的真实数据在CM-5并行计算机上进行的实验工作表明，在不影响重建图像质量的情况下，相对于EM-ML算法的时间和空间要求，可以实现相当可观的节省。