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Theses

Estimation et classification des temps de relaxation multi-exponentiels en IRM. Application aux tissus végétaux

Abstract : Acquired relaxation data in magnetic resonance imaging makes it possible to conduct fine analysis of tissues composition. Conventionally, the analysis is realized by adopting a mono-exponential model at each voxel of the image, yet, a multi-exponential decay model may provide richer information. However, obtaining and interpreting multi-exponential relaxation time maps at the whole image level, from magnitude MRI images, requires solving a large scale inverse problem. This thesis work proposes algorithms for multiexponential relaxation times and their associated intensities maps reconstruction. These algorithms are based on the maximum-likelihood estimator under the hypothesis of a Rician noise distribution, case of magnitude images, and a spatial regularization favoring the regularity of the maps. The resulting large-scale optimization problem is solved using an iterative descent approach by majorization-minimization coupled with a Levenberg-Marquardt algorithm with step search. Finally, we propose a method for image composition characterization from the estimated parameters using classification algorithms. The developed algorithms in this thesis are applied to vegetal tissue analysis.
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Submitted on : Wednesday, September 30, 2020 - 4:00:17 PM
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Christian El Hajj. Estimation et classification des temps de relaxation multi-exponentiels en IRM. Application aux tissus végétaux. Traitement du signal et de l'image [eess.SP]. École centrale de Nantes, 2019. Français. ⟨NNT : 2019ECDN0066⟩. ⟨tel-02561480v2⟩

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