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Bayesian iterative reconstruction methods for 3D X-ray Computed Tomography / Camille Chapdelaine (2019)  

Public Titre : Bayesian iterative reconstruction methods for 3D X-ray Computed Tomography Type de document : Thèse/HDR Auteurs : Camille Chapdelaine, Auteur ; Charles Soussen, Directeur de thèse Editeur : Paris-Orsay : Université de Paris 11 Paris-Sud Centre d'Orsay Année de publication : 2019 Importance : 185 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de Doctorat de l’Université Paris - Saclay préparée à l'Université Paris-Sud, Sciences et Technologies de l’Information et de la Communication (STIC), Traitement du signal et des images Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] artefact [Termes IGN] faisceau [Termes IGN] inférence [Termes IGN] itération [Termes IGN] processeur graphique [Termes IGN] rayon X [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'image [Termes IGN] segmentation [Termes IGN] spectroscopie [Termes IGN] théorème de Bayes [Termes IGN] tomographie [Termes IGN] volume (grandeur) Index. décimale : THESE Thèses et HDR Résumé : (auteur) In industry, 3D X-ray Computed Tomography aims at virtually imaging a volume in order to inspect its interior. The virtual volume is obtained thanks to a reconstruction algorithm based on projections of X-rays sent through the industrial part to inspect. In order to compensate uncertainties in the projections such as scattering or beam-hardening, which are cause of many artifacts in conventional filtered backprojection methods, iterative reconstruction methods bring further information by enforcing a prior model on the volume to reconstruct, and actually enhance the reconstruction quality. In this context, this thesis proposes new iterative reconstruction methods for the inspection of aeronautical parts made by SAFRAN group. In order to alleviate the computational cost due to repeated projection and backprojection operations which model the acquisition process, iterative reconstruction methods can take benefit from the use of high-parallel computing on Graphical Processor Unit (GPU). In this thesis, the implementation on GPU of several pairs of projector and backprojector is detailed. In particular, a new GPU implementation of the matched Separable Footprint pair is proposed. Since many of SAFRAN's industrial parts are piecewise-constant volumes, a Gauss-Markov-Potts prior model is introduced, from which a joint reconstruction and segmentation algorithm is derived. This algorithm is based on a Bayesian approach which enables to explain the role of each parameter. The actual polychromacy of X-rays, which is responsible for scattering and beam-hardening, is taken into account by proposing an error-splitting forward model. Combined with Gauss-Markov-Potts prior on the volume, this new forward model is experimentally shown to bring more accuracy and robustness. At last, the estimation of the uncertainties on the reconstruction is investigated by variational Bayesian approach. In order to have a reasonable computation time, it is highlighted that the use of a matched pair of projector and backprojector is necessary. Note de contenu : 1- X-ray computed tomography : an inverse problem 2- Reconstruction methods in X-ray computed tomography 3- Projection and backprojection operators 4- Gauss-Markov-Potts prior model for joint reconstruction and segmentation 5- Error-splitting forward model and its application with Gauss-Markov-Potts prior 6- Towards the estimation of the uncertainties on the reconstruction by Variational Bayesian Approach 7- Conclusion and perspectives Numéro de notice : 25702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences et Technologies de l’Information et de la Communication (STIC) : Traitement du signal et des images : Paris 11 : 2019 Organisme de stage : Safran nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02110033 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94827