Neutron computed tomography (NCT), a 3D non-destructive characterization technique, is carried out at nuclear reactor or spallation neutron source-based user facilities. Because neutrons are not severely attenuated by heavy elements and are sensitive to light elements like hydrogen, neutron radiography and computed tomography offer a complementary contrast to x-ray CT conducted at a synchrotron user facility. However, compared to synchrotron x-ray CT, the acquisition time for an NCT scan can be orders of magnitude higher due to lower source flux, low detector efficiency and the need to collect a large number of projection images for a high-quality reconstruction when using conventional algorithms. As a result of the long scan times for NCT, the number and type of experiments that can be conducted at a user facility is severely restricted. Recently, several deep convolutional neural network (DCNN) based algorithms have been introduced in the context of accelerating CT scans that can enable high quality reconstructions from sparse-view data. In this paper, we introduce DCNN algorithms to obtain high-quality reconstructions from sparse-view and low signal-to-noise ratio NCT data-sets thereby enabling accelerated scans. Our method is based on the supervised learning strategy of training a DCNN to map a low-quality reconstruction from sparse-view data to a higher quality reconstruction. Specifically, we evaluate the performance of two popular DCNN architectures—one based on using patches for training and the other on using the full images for training. We observe that both the DCNN architectures offer improvements in performance over classical multi-layer perceptron as well as conventional CT reconstruction algorithms. Our results illustrate that the DCNN can be a powerful tool to obtain high-quality NCT reconstructions from sparse-view data thereby enabling accelerated NCT scans for increasing user-facility throughput or enabling high-resolution time-resolved NCT scans.

中子计算机断层扫描 (NCT) 是一种 3D 非破坏性表征技术，在核反应堆或散裂中子源的用户设施中进行。由于中子不会被重元素严重衰减并且对氢等轻元素敏感，因此中子射线照相术和计算机断层扫描术提供了与在同步加速器用户设施进行的 X 射线 CT 的互补对比。然而，与同步加速器 X 射线 CT 相比，由于源通量较低、探测器效率低以及需要收集大量投影图像以进行高质量重建，NCT 扫描的采集时间可能要高几个数量级。使用常规算法。由于 NCT 的扫描时间长，可以在用户设施中进行的实验的数量和类型受到严格限制。最近，在加速 CT 扫描的背景下，已经引入了几种基于深度卷积神经网络 (DCNN) 的算法，这些算法可以从稀疏视图数据中进行高质量的重建。在本文中，我们介绍了 DCNN 算法，以从稀疏视图和低信噪比的 NCT 数据集获得高质量的重建，从而实现加速扫描。我们的方法基于训练 DCNN 以将低质量重建从稀疏视图数据映射到更高质量重建的监督学习策略。具体来说，我们评估了两种流行的 DCNN 架构的性能——一种基于使用补丁进行训练，另一种基于使用完整图像进行训练。我们观察到，与经典的多层感知器以及传统的 CT 重建算法相比，DCNN 架构都提供了性能改进。我们的结果表明，DCNN 可以成为从稀疏视图数据中获得高质量 NCT 重建的强大工具，从而实现加速 NCT 扫描以提高用户设施吞吐量或实现高分辨率时间分辨 NCT 扫描。