X-ray tomography reconstruction based on Deep-Learning methods


CEA-LIST is developing the CIVA software platform, a benchmark for the simulation of non-destructive testing processes. In particular, it offers tools for X-ray and tomographic inspection which, for a given inspection, can simulate all radiographies, taking into account various associated physical phenomena, as well as the corresponding tomographic reconstruction. CEA-LIST also has an experimental platform for robotized X-ray tomography inspection. The proposed work is part of the laboratoryapos;s contribution to a bilateral French-German ANR project involving academic and industrial partners, focusing on the inspection of large-scale objects using the robotized platform. A sufficient number of X-rays must be taken in order to carry out a 3D reconstruction of the object. In many situations, some angles of view cannot be acquired due to the dimensions of the object and/or the motion limitations of the robots used, resulting in a loss of quality in the 3D reconstruction. Expected contributions focus on the use of Deep-Learning methods, to complete missing projections on the one hand, and reduce reconstruction artifacts on the other. This work includes the CIVA-based steps of building a simulated database and evaluating the obtained results using POD (Probability Of Detection) measurements. The candidate will have access to the facilities of the Paris Saclay research center and will be expected to promote his/her results in the form of scientific communications (international conferences, publications). Candidate profile: PhD in data processing or artificial intelligence. Fluent English (oral presentations, scientific publications). Previous knowledge of X-ray physics and tomographic reconstruction methods would be appreciated.

Docteur en traitement de données ou intelligence artificielle


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