X-ray Computed Tomography (CT) has been increasingly used in many industrial domains for its unique capability of con- trolling both the integrity and dimensional conformity of parts. Still, it fails to be adopted as a standard technique for on-line mon- itoring 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 recon- struction 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 meth- ods, 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 spar- sity) ; showing that efficient CT inspection can be performed from only few dozens of images