[en] Among numerous techniques for non-destructive evaluation (NOE), X-rays systems are well suited to inspect inner objects. Acquiring several radiographs of inspected objects under different points of view enables to recover a three dimensional structural information. In this NOE application, a tomographic testing is considered. This work deals with two tomographic testing optimizations in order to improve the characterization of defects that may occur into metallic welds. The first one consists in the optimization of the acquisition strategy. Because tomographic testing is made on-line, the total duration for image acquisition is fixed, limiting the number of available views. Hence, for a given acquisition duration, it is possible either to acquire a very limited number of radiographs with a good signal to noise ratio in each single acquisition or a larger number of radiographs with a limited signal to noise ratio. The second one consists in optimizing the 3D reconstruction algorithms from a limited number of cone-beam projections. To manage the lack of data, we first used algebraic reconstruction algorithms such as ART or regularized ICM. In terms of acquisition strategy optimization, an increase of the number of projections was proved to be valuable. Taking into account specific prior knowledge such as support constraint or physical noise model in attenuation images also improved reconstruction quality. Then, a new regularized region based reconstruction approach was developed. Defects to reconstruct are binary (lack of material in a homogeneous object). As a consequence, they are entirely described by their shapes. Because the number of defects to recover is unknown and is totally arbitrary, a level set formulation allowing handling topological changes was used. Results obtained with a regularized level-set reconstruction algorithm are optimistic in the proposed context. (author)