Users of X-ray (micro-)CT in research environments often study many different types of objects, with many different research questions. For each new scan, the settings of the scan (number of angles, dose, cone angle) are chosen by the user, often based on how much time is available, the dose sensitivity of the sample, and geometrical characteristics of the particular CT-scanner that is used. The FDK algorithm is the most common reconstruction method used for circular cone-beam data. Its filter is typically chosen based on characteristics of the object, the scan parameters, and task-specific metrics. This imposes a problem for case-by-case research use, as selecting an optimal filter requires manual and subjective user choices as well as considerable expertise. In this article we present a computationally efficient and automated method to compute an FDK-filter for a given measured projection dataset that is optimal with respect to an objectively defined quality criterion that is based on the difference between the measured projection data and the computed projections of the reconstructed volume. We show that for a variety of objects, scan settings (number of angles and noise levels), and tasks (porosity quantification, threshold-based segmentation), the FDK-filters computed by our approach yield accurate results in terms of several different metrics that are comparable to filters manually selected for the experiments.

中文翻译：

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研究环境中锥束 CT 的自动 FDK 滤波器选择

在研究环境中使用 X 射线（微）CT 的用户经常研究许多不同类型的物体，以及许多不同的研究问题。对于每次新扫描，用户通常根据可用时间、样本的剂量敏感性和特定 CT 的几何特征来选择扫描设置（角度数、剂量、锥角）。使用的扫描仪。FDK 算法是最常用的圆锥波束数据重建方法。它的过滤器通常是根据对象的特征、扫描参数和特定于任务的指标来选择的。这给逐案研究使用带来了问题，因为选择最佳过滤器需要手动和主观的用户选择以及相当多的专业知识。在本文中，我们提出了一种计算效率高且自动化的方法来为给定的测量投影数据集计算 FDK 滤波器，该方法对于客观定义的质量标准是最佳的，该标准基于测量投影数据和计算投影之间的差异重建的体积。我们表明，对于各种对象、扫描设置（角度和噪声水平的数量）和任务（孔隙度量化、基于阈值的分割），我们的方法计算的 FDK 过滤器根据几种不同的指标产生准确的结果，与为实验手动选择的过滤器相当。