Neutron Computed Tomography (CT) is an increasingly utilised non-destructive analysis tool in material science, palaeontology, and cultural heritage. With the development of new neutron imaging facilities (such as DINGO, ANSTO, Australia) new opportunities arise to maximise their performance through the implementation of statistically driven image reconstruction methods which have yet to see wide scale application in neutron transmission tomography. This work outlines the implementation of a convex algorithm statistical image reconstruction framework applicable to the geometry of most neutron tomography instruments with the aim of obtaining similar imaging quality to conventional ramp filtered back-projection via the inverse Radon transform, but using a lower number of measured projections to increase object throughput. Through comparison of the output of these two frameworks using a tomographic scan of a known 3 material cylindrical phantom obtain with the DINGO neutron radiography instrument (ANSTO, Australia), this work illustrates the advantages of statistical image reconstruction techniques over conventional filter back-projection. It was found that the statistical image reconstruction framework was capable of obtaining image estimates of similar quality with respect to filtered back-projection using only 12.5% the number of projections, potentially increasing object throughput at neutron imaging facilities such as DINGO eight-fold.
Neural-network-based material identification in photon-counting computed tomography using region-of-interest spectral features
International Sakharov Environmental Institute of Belarusian State University（白俄罗斯国立大学萨哈罗夫环境研究所）；Joint Institute for Nuclear Research（联合核子研究所）；Lomonosov Moscow State University（莫斯科国立大学）
Monochromatic computed tomography using laboratory-scale setup: proof-of-concept
在这篇文章中，我们证明了高度单色全场X射线吸收近边缘结构的断层扫描的可行性，使用实验室规模的约翰型X射线吸收光谱仪的基础上，传统的X射线管源。在此概念验证中，通过使用嵌入元素Se、Na $_2$ SeO $_3$和Na $_2$ SeO $_4$的体模，我们证明了不同氧化态的Se的三维分布可以通过吸收边衬度断层扫描从体模矩阵和彼此进行映射和区分。所提出的方法允许使用低亮度X射线源在毫米级样品中进行化学形态的体积分析，并且代表了包括生物和化学在内的许多领域中的材料工程和研究的新分析工具。
Local Fiber Orientation from X-ray Region-of-Interest Computed Tomography of large Fiber Reinforced Composite Components
Ford Research and Innovation Center Aachen, Ford-Werke GmbH（福特汽车公司亚琛福特研究与创新中心）；Hochschule Darmstadt（达姆施塔特应用技术大学）；Fraunhofer-Institut für Techno-und Wirtschaftsmathematik（弗劳恩霍夫工业数学研究所）；Fraunhofer-Entwicklungszentrum Röntgentechnik（弗劳恩霍夫X射线技术开发中心）；Montaplast GmbH（蒙塔普拉斯有限公司）
Unsupervised denoising for sparse multi-spectral computed tomography
University of Oulu（奥卢大学）；University College London（伦敦大学学院）
具有光子计数探测器（PCD）的多能量计算机断层扫描（CT）能够实现光谱成像，因为PCD可以将入射光子分配给特定的能量通道。然而，具有许多光谱通道的PCD大幅增加了CT重建的计算复杂度，并且定制重建算法需要根据不同的噪声统计进行微调。\rev{Especially if many projections are taken, a large amount of data has to be collected and stored. Sparse view CT is one solution for data reduction. However, these issues are especially exacerbated when sparse imaging scenarios are encountered due to a significant reduction in photon counts.}在这项工作中，我们研究了基于学习的改进，以获得高质量的重建从稀疏测量的64通道PCD-CT的挑战性任务的适用性。特别是，为了克服训练过程中丢失的参考数据，我们提出了一种无监督的去噪和伪影去除方法，通过在重建中利用不同的滤波器函数以及光谱通道与核范数的显式耦合。性能评估模拟合成数据和公开提供的实验多光谱成像通过计算机断层扫描（MUSIC）数据集。我们将我们的无监督方法的质量…
