Iterative reconstruction algorithm comparison using Poisson noise distributed sinogram data in passive gamma emission tomography
Iterative reconstruction algorithm comparison using Poisson noise distributed sinogram data in passive gamma emission tomography
复制标题
DOI：
10.1080/00223131.2020.1854882
复制
发表时间：
2020-12-13
影响因子：
1.2
通讯作者：
Sagara, Hiroshi
中科院分区：
文献类型：
--
作者：
Shiba, Shigeki;Sagara, Hiroshi
伽马发射断层扫描（GET）检测发射伽马射线的燃料棒，可能用作核保障监督的核查工具。在GET中，迭代重建算法通常用于重建被动伽马射线发射源分布。通常，使用有噪声的正弦图数据，迭代重建算法通过迭代增加重建图像上的噪声水平。因此，重要的是评估代表性迭代重建算法的性能如何随着噪声正弦图数据而变化。采用梯度法、最速下降法、共轭梯度法、代数重建技术、同步迭代重建技术和最大似然期望最大化（MLEM）等算法，重建了含有缺失燃料棒的水-水反应堆（WWER）燃料组件模型内部的被动γ射线源分布。因此，MLEM算法产生了一个更高的对比度重建图像，被认为是更可靠的算法来区分燃料棒的被动γ射线发射源分布。
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.