Diffusion directions imaging : reconstruction haute résolution des faisceaux de matière blanche par IRM de diffusion basse résolution angulaire

Aymeric Stamm 1
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : The objective of this thesis is to provide a complete pipeline that achieves an accurate reconstruction of the white matter fascicles using clinical diffusion images characterized by a low angular resolution. This involves (i) a diffusion model inferred in each voxel from the diffusion images and (ii) a tractography algorithm fed with these local models to perform the actual reconstruction of fascicles. Our contribution in diffusion modeling is a new statistical distribution, the properties of which are extensively studied. We model the diffusion as a mixture of such distributions, for which we design a model selection tool that estimates the number of mixture components. We show that the model can be accurately estimated from single shell low angular resolution diffusion images and that it provides specific biomarkers for studying tumors. Our contribution in tractography is an algorithm that approximates the distribution of fascicles emanating from a seed voxel. We achieve that by means of a particle filter better adapted to multi-modal distributions than the traditional filters. To demonstrate the clinical applicability of our tools, we participated to all three editions of the MICCAI DTI Tractography challenge aiming at reconstructing the cortico-spinal tract from single-shell low angular and low spatial resolution diffusion images. Results show that our pipeline provides a reconstruction of the full extent of the CST.
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Aymeric Stamm. Diffusion directions imaging : reconstruction haute résolution des faisceaux de matière blanche par IRM de diffusion basse résolution angulaire. Other [cs.OH]. Université Rennes 1, 2013. English. ⟨NNT : 2013REN1S168⟩. ⟨tel-00999556⟩

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