Evaluation and Comparison of Two Deep-Learning Strategies for On-Line X-Ray Computed Tomography

X-ray Computed Tomography (CT) has been increasingly used in many industrial domains for its unique capability of controlling both the integrity and dimensional conformity of parts. Still, it fails to be adopted as a standard technique for on-line monitoring due to its excessive cost in terms of acquisition time. The reduction of the number of projections, leading to the so-called sparse-view CT strategy, while maintaining a sufficient reconstruction quality is therefore one of the main challenges in this field. This work aims to evaluate and compare the performances of two deep learning strategies for the sparse-view reconstruction problem. As such, we propose an extensive study of these methods, both in terms of data regime and angular sparsity during training. The two strategies present quantitative improvements over a classical FBP/FDK approach with a PSNR improvement varying between 11 and 16 dB (depending on the angular sparsity) ; showing that efficient CT inspection can be performed from only few dozens of images