ABSTRACT

Gamma emission tomography (GET) detects fuel rods that emit gamma rays for potential use as verification tools in nuclear safeguards. In GET, iterative reconstruction algorithms are often used to reconstruct passive gamma-ray emitter source distributions. Generally, using noisy sinogram data, the iterative reconstruction algorithms increase the noise level on a reconstruction image with iterations. Thus, it is important to evaluate how performances of representative iterative reconstruction algorithms change with noisy sinogram data. The passive gamma-ray source distributions inside the mock-up of the water-water energetic reactor (WWER) fuel assembly having missing fuel rods were reconstructed by using the following algorithms: gradient method, steepest descent method, conjugate gradient reconstruction method, algebraic reconstruction technique, simultaneous iterative reconstruction technique, and maximum likelihood expectation maximization (MLEM). Consequently, MLEM algorithm yielded a higher contrast reconstruction image and was regarded as higher reliable algorithm to discriminate the fuel rods from the passive gamma-ray emitter source distribution.

摘要

伽马射线发射断层扫描（GET）可以检测出发射伽马射线的燃料棒，以用作核保障中的验证工具。在GET中，迭代重建算法通常用于重建无源伽马射线发射器源分布。通常，使用嘈杂的正弦图数据，迭代重建算法会通过迭代增加重建图像上的噪声级别。因此，重要的是要评估代表性迭代重建算法的性能如何随嘈杂的正弦图数据而变化。使用以下算法重建缺少燃料棒的水-水高能反应堆（WWER）燃料组件模型内部的被动伽马射线源分布：梯度方法，最速下降方法，共轭梯度重建方法，代数重建技术，同时迭代重建技术和最大似然期望最大化（MLEM）。因此，MLEM算法产生了更高的对比度重建图像，并被认为是从被动伽马射线发射源分布中区分燃料棒的更高可靠性算法。