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Modèles de régression multivariés pour la comparaison de populations en IRM de diffusion

Abstract : Diffusion Tensor MRI (DT-MRI) is an imaging modality that allows to study in vivo the structure of white matter fibers through the characterization of diffusion properties of water molecules in the brain. This work focused on group comparison in DT-MRI. The aim is to identify white matter regions whose structural properties are statistically different between two populations or significantly correlated with some explanatory variables. The challenge is to locate and characterize lesions caused by a disease and to understand the underlying mechanisms. To this end, we proposed several voxel-based strategies that rely on the General Linear Model (GLM) and its multivariate and manifold-based extensions, to perform statistical tests that explicitly incorporate explanatory variables. In DT-MRI, diffusion of water molecules can be modeled by a second order tensor represented by a three dimensional symmetric and positive definite matrix. The main contribution of this thesis was to demonstrate the added value of considering the full tensor information as compared to a single scalar index characterizing some diffusion properties (fractional anisotropy or mean diffusion) in the GLM, as it is usually done in neuroimaging studies. Several strategies for extending the GLM to tensor were compared, either in terms of statistical hypothesis (homoscedasticity vs heteroscedasticity), or metrics used for parameter estimation (Euclidean, Log-Euclidean and Riemannian), or the way to take into account the spatial neighborhood information. We also studied the influence of some pre-processing such as filtering and registration. Finally, we proposed a method for characterizing the detected regions in order to facilitate their physiopathological interpretation. Validations have been conducted on synthetic data as well as on a cohort of patients suffering from Neuromyelitis Optica.
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Alix Bouchon. Modèles de régression multivariés pour la comparaison de populations en IRM de diffusion. Traitement du signal et de l'image [eess.SP]. Université de Strasbourg, 2016. Français. ⟨NNT : 2016STRAD035⟩. ⟨tel-01548466⟩

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