Abstract 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 × 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.

中文翻译：

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具有有序子集期望最大化方法的被动伽马发射断层扫描

摘要 伽马射线发射断层扫描 (GET) 被开发为一种潜在的验证工具，用于可视化燃料棒的被动伽马射线发射源。在 GET 中，最大似然-期望最大化 (MLEM) 被用作迭代重建方法。但由于算法中的收敛迭代与像素大小成正比，因此收敛速度慢，实际应用的计算成本高。因此，使用有序子集期望最大化方法（OSEM），重建了 BWR 10 × 10 模型燃料组件的棒状相对伽马射线发射器分布，以评估 OSEM 的性能。OSEM 实现了与 MLEM 相当的重建，并有效减少了迭代次数。