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Reconstruction statistique 3D à partir d’un faible nombre de projections : application : coronarographie RX rotationnelle

Abstract : The problematic of this thesis concerns the statistical iterative 3D reconstruction of coronary tree from a very few number of coronary angiograms (5 images). During RX rotational angiographic exam, only projections corresponding to the same cardiac phase are selected in order to check the condition of space and time non-variability of the object to reconstruct (static reconstruction). The limited number of projections complicates the reconstruction, considered then as an illness inverse problem. The answer to a similar problem needs a regularization process. To do so, we choose baysian formalism considering the reconstruction as a random field maximizing the posterior probability (MAP), composed by quadratic likelihood terms (attached to data) and Gibbs prior (prior markovian based on a partial interpretation of the object to reconstruct). The MAP maximizing allowed us using a numerical optimization algorithm, to introduce a smoothing constraint and preserve the edges on the reconstruction while choosing wisely the potential functions associated to prior energy. In this paper, we have discussed in details the three components of efficient statistical reconstruction MAP, which are : 1- the construction of precise physical model of acquisition process; 2- the selection of an appropriate prior model; and 3- the definition of an efficient iterative optimization algorithm. This discussion lead us to propose two iterative algorithms MAP, MAP-MNR and MAP-ARTUR-GC, which we have tested and evaluated on realistic simulated data (Patient data from 64-slice CT).
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Ahmed Oukili. Reconstruction statistique 3D à partir d’un faible nombre de projections : application : coronarographie RX rotationnelle. Traitement du signal et de l'image [eess.SP]. Université Rennes 1, 2015. Français. ⟨NNT : 2015REN1S109⟩. ⟨tel-01317532⟩

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