Fast algorithms based on Empirical Interpolation Methods for selecting best projections in Sparse-View X-ray Computed Tomography using a priori information

Abstract

To ensure the safety of critical parts in mass production, non-destructive techniques are increasingly considered. Industrial Computed Tomography (CT) is a unique tool to inspect a part integrity and dimensional conformity. However, it usually requires many radiographies (also called projections) to reconstruct a 3D CT image accurately. To reconstruct an object from a reduced number of projections has become one of the most crucial challenges in X-ray CT. Iterative reconstruction algorithms have relieved robotic CT systems from usual circular or helical trajectories, allowing them to focus on the most informative and relevant projections. In this paper, we propose an approach to select the best projections for X-ray tomographic reconstructions when a priori information on the inspected object is available. The presented algorithms are based on Empirical Interpolation Methods and show a good reconstruction quality from a dozen of projections. Those methods have been selected for their flexibility, speed, and the perspectives they open up for sinogram inpainting.