Best projections selection algorithm based on constrained QDEIM for sparse-views X-ray Computed Tomography

Abstract

X-ray Computed Tomography (CT) is a non-destructive testing tool increasingly used by manufacturers, with growing interest in in-line testing applications. However, it is still struggling to establish itself as a standard technique due to long acquisition times. In order to meet industrial imperatives, recent research aims at reducing this acquisition time by limiting the number of views while keeping a good CT image quality. The rise of iterative reconstruction methods has made it possible to partially solve the Sparse-View CT problem thanks to the injection of regularisation terms. Furthermore, these methods also allow more freedom in the acquisition trajectories. In this work, we propose a method for reducing the number of projections without impacting the reconstruction quality by acquiring only the most relevant views. Indeed, not all views provide the same amount of information. We, therefore, present a technique for selecting views when the geometry of the inspected object is roughly known a priori. Our method is based on the Q-Discrete Empirical Interpolation Method (QDEIM) and considers the attenuation of the rays as an additional constraint.