Approches statistiques pour la détection de changements en IRM de diffusion : application au suivi longitudinal de pathologies neuro-dégénératives

Abstract : Diffusion MRI is a new medical imaging modality of great interest in neuroimaging research. This modality enables the characterization in vivo of local micro-structures. Tensors have commonly been used to model the diffusivity profile at each voxel. This multivariate data set requires the design of new dedicated image processing techniques. The context of this thesis is the automatic analysis of intra-patient longitudinal changes with application to the follow-up of neuro-degenerative pathologies. Our research focused on the development of new models and statistical tests for the automatic detection of changes in temporal sequences of diffusion images. Thereby, this thesis led to a better modeling of second order tensors (statistical tests on positive definite matrices), to an extension to higher-order models, and to the definition of refined neighborhoods on which tests are conducted, in particular the design of statistical tests on fiber bundles.
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Antoine Grigis. Approches statistiques pour la détection de changements en IRM de diffusion : application au suivi longitudinal de pathologies neuro-dégénératives. Traitement du signal et de l'image [eess.SP]. Université de Strasbourg, 2012. Français. ⟨NNT : 2012STRAD018⟩. ⟨tel-00750933⟩

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