Skip to Main content Skip to Navigation
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.
Complete list of metadatas

Cited literature [75 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-02561480
Contributor : Saïd Moussaoui <>
Submitted on : Monday, May 4, 2020 - 1:09:22 AM
Last modification on : Thursday, May 14, 2020 - 12:44:00 PM

File

Manuscrit_christian_el-hajj.pd...
Files produced by the author(s)

Identifiers

  • HAL Id : tel-02561480, version 1

Citation

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]. Ecole Centale de Nantes, 2019. Français. ⟨tel-02561480⟩

Share

Metrics

Record views

59

Files downloads

21