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.

doi.org/10.1109/TCI.2020.2971136
IEEE Transactions on Computational Imaging
Computational Imaging

Lagerwerf, M.J, Palenstijn, W.J, Kohr, H, & Batenburg, K.J. (2020). Automated FDK-filter selection for cone-beam CT in research environments. IEEE Transactions on Computational Imaging, 6, 739–748. doi:10.1109/TCI.2020.2971136