Passive gamma emission tomography with ordered subset expectation maximization method
Passive gamma emission tomography with ordered subset expectation maximization method
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DOI：
10.1016/j.anucene.2020.107823
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发表时间：
2021-01-01
影响因子：
1.9
通讯作者：
Sagara, Hiroshi
中科院分区：
文献类型：
--
作者：
Shiba, Shigeki;Sagara, Hiroshi
伽马射线发射断层成像（GET）是作为一种潜在的核查工具，以可视化的被动伽马射线发射源的燃料棒。在GET中，最大似然期望最大化（MLEM）被用作迭代重建方法。但由于该算法的收敛迭代与像素大小成正比，收敛速度慢，实际应用的计算代价高。因此，使用有序子集期望最大化方法（OSEM），并重建BWR 10 x 10模型燃料组件的棒方向相对伽马射线发射体分布，以评估OSEM的性能。OSEM使重建与MLEM相当，有效减少了迭代次数。(C)2020爱思唯尔有限公司保留所有权利。
Gamma-ray emission tomography (GET) was developed as a potential verification tool to visualize the passive gamma-ray emitter sources of fuel rods. In GET, maximum likelihood-expectation maximization (MLEM) was employed as an iterative reconstruction method. However, as convergence iteration in the algorithm is proportional to the pixel size, convergence is slow and the calculation cost for practical application is high. Therefore, an ordered subset expectation maximization method (OSEM) was used, and the rod-wise relative gamma-ray emitter distribution of a BWR 10 x 10 mock-up fuel assembly was reconstructed to evaluate the performance of the OSEM. The OSEM enabled reconstruction comparable to that of MLEM with an effective decrease in the number of iterations. (C) 2020 Elsevier Ltd. All rights reserved.