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Metrics Distribution Analyses : definition and evaluation of a new method for the microstructural characterisation of the brain of stroke patients

Lovat, Louis
(2023)

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Lovat_08531800_2023.pdf
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Details

Supervisors
Macq, Benoît ; Dricot, Laurence
Faculty
Ecole polytechnique de Louvain
Degree label
Master [120] : ingénieur civil mécanicien, à finalité spécialisée
Abstract
Stroke is one of the world’s leading causes of disability. The use of magnetic resonance imaging (MRI), and more specifically of diffusion magnetic resonance imaging (dMRI), made it possible to carry out an in-depth analysis of the impact of stroke on the brain of victims. The development of diffusion models such as DTI, NODDI, DIAMOND and MF has enabled precise in-vivo characterisation of brain microstructures. With it, we have acquired the ability to link the changes in these microstructures to their visible effect in patients motor skills using clinical tests. There are many ways of analysing the results of diffusion models, most of which coarsely average the values of metrics over entire brain regions. The aim of this study is to establish a new method called Metrics Distribution Analyses (MDA), which aims to better represent diffusion metrics in a region of interest. We tested this method using a database composed of 104 observations from 32 patients suffering from ischaemic stroke, each observation consisting of a diffusion image, a T1-weighted image and a result from a clinical test: the 6-minute walk test. Using these data, we computed diffusion metric maps over the corticospinal tract of patients and demonstrated the effectiveness of our analyses. We also compared the new method with more traditional ones to put the results in perspective. Our results showed great potential in the use of MDA with the DTI and DIAMOND metrics and less interest in the NODDI and MF metrics. Even though the statistical significance of some of our results suffers from the limitations of this study and could be improved, we concluded that the use of MDA together with other traditional methods could undoubtedly improve the analysis of microstructures based on diffusion images.