A maximum likelihood expectation maximization algorithm with thresholding
A maximum likelihood expectation maximization algorithm with thresholding
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DOI：
10.1016/j.compmedimag.2005.04.003
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发表时间：
2005-10-01
影响因子：
5.7
通讯作者：
Fu, YK
中科院分区：
文献类型：
--
作者：
Chuang, KS;Jan, ML;Fu, YK
在图像重建中，最大似然期望最大化(MLEM)算法比传统的滤波反投影(FBP)算法具有许多优点。然而，该算法的收敛速度慢、计算量大，限制了其在临床上的应用。这项研究提出了在MLEM和有序子集EM(OSEM)算法中结合阈值技术来加速收敛。阈值设置为c*m，其中m是整个图像的平均像素值。重建时间与总像素数成正比，因此，如果像素值低于阈值，则阈值技术使其无效，可以有效地去除非活动像素，并显著加速重建。对模拟的PET数据的初步测试表明，阈值技术加快了收敛速度，减少了重建图像的误差。重建性能随着阈值水平的提高而提高，当c值约为1时，均方误差达到最小值。(C)2005年由Elsevier Ltd.发布。
The maximum likelihood expectation maximization (MLEM) algorithm has several advantages over the conventional filtered back-projection (FBP) for image reconstruction. However, the slow convergence and the high computational cost for its practical implementation have limited its clinical applications. This study proposes the incorporation of a thresholding technique in both the MLEM and ordered subsets EM (OSEM) algorithm to accelerate convergence. The threshold is set to c*m, where m is the mean pixel value of the whole image. The reconstruction time is proportional to the total number of pixels, so a thresholding technique that nullifies the value of a pixel if it falls below a threshold, can effectively remove the non-active pixels and substantially accelerate reconstruction. Preliminary tests on simulated PET data reveal that the thresholding technique accelerates the convergence rate and reduce error in the reconstructed image. The reconstruction performance improves with the increase of the threshold level and the MSE reaches minimum for c value equals to about 1. (c) 2005 Published by Elsevier Ltd.