Traitement de données multi-spectrales par calcul intensif et applications chez l'homme en imagerie par résonnance magnétique nucléaire

Abstract : As a non-invasive technology for studying brain imaging, functional magnetic resonance imaging (fMRI) has been employed to understand the brain underlying mechanisms of food intake. Using liquid stimuli to fake food intake adds difficulties which are not present in fMRI studies with visual stimuli. This PhD thesis aims to propose a robust method to analyse food stimulated fMRI data. To correct the data from swallowing movements, we have proposed to censure the data uniquely from the measured signal. We have also improved the normalization step of data between subjects to reduce signal loss.The main contribution of this thesis is the implementation of Ward's algorithm without data reduction. Thus, clustering the whole brain in several hours is now feasible. Because Euclidean distance computation is the main part of Ward algorithm, we have developed a cache-aware algorithm to compute the distance between each pair of voxels. Then, we have parallelized this algorithm for three architectures: shared-memory architecture, distributed memory architecture and NVIDIA GPGPU. Once Ward's algorithm has been applied, it is possible to explore multi-scale clustering of data. Several criteria are considered in order to evaluate the quality of clusters. For a given number of clusters, we have proposed to compute connectivity maps between clusters or to compute Pearson correlation coefficient to identify brain regions activated by the stimulation.
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Mélodie Angeletti. Traitement de données multi-spectrales par calcul intensif et applications chez l'homme en imagerie par résonnance magnétique nucléaire. Bio-informatique [q-bio.QM]. Université Clermont Auvergne, 2019. Français. ⟨NNT : 2019CLFAC004⟩. ⟨tel-02172043⟩

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