Best projections selection algorithm based on constrained QDEIM for sparse-views X-ray Computed Tomography
Résumé
X-ray Computed Tomography 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 due to long acquisition times. Industrial imperatives force us to reduce the number of views. 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 our work, to reduce the number of projections without impacting the reconstruction quality, we have not optimised the reconstruction but rather the choice of 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 known a priori. Our method is based on the Q-Discrete Empirical Interpolation Method and considers the attenuation of the rays.