Fang Ming, Altmann Yoann, Della Latta Daniele, Salvatori Massimiliano, Di Fulvio Angela
Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, US.
School of Engineering and Physical Sciences, Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS, UK.
Sci Rep. 2021 Jan 28;11(1):2442. doi: 10.1038/s41598-021-82031-8.
Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.
通过核保障措施来监测成员国对《不扩散核武器条约》的遵守情况。被动伽马发射断层扫描（PGET）系统是在国际原子能机构（IAEA）项目JNT 1510框架内开发的一种新型仪器，该项目包括欧盟委员会、芬兰、匈牙利和瑞典。PGET用于核查存储在水池中的乏核燃料。先进的图像重建技术对于获取乏燃料束的高质量横截面图像至关重要，以便国际原子能机构的检查人员监测核材料并及时识别其转移情况。在这项工作中，我们开发了一套软件套件，用于准确重建乏燃料横截面图像、自动识别现有的燃料棒并估计其活度。由于乏燃料的高活度和自吸收，其测量给图像重建带来了独特的挑战。虽然前者通过探测器物理准直得到缓解，但我们实现了一个线性前向模型来模拟探测器对PGET内燃料棒的响应，以解决后者的问题。图像重建通过使用快速迭代收缩阈值算法求解正则化线性逆问题来进行。我们还实现了基于逆拉东变换的传统滤波反投影（FBP）方法用于比较，并将这两种方法应用于重建模拟模型燃料组件的图像。逆问题方法获得了更高的图像分辨率和更少的重建伪影，均方误差降低了50%，结构相似性提高了200%。然后，我们使用卷积神经网络（CNN）自动识别束类型并从图像中提取销钉位置；最后将估计的活度水平与真实值进行比较。所提出的计算方法准确估计了现有销钉的活度水平，相关不确定性约为5%。
