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Modeling and simulation of the diffusion MRI signal from human brain white matter to decode its microstructure and produce an anatomic atlas at high fields (3T)

Abstract : Diffusion Magnetic Resonance Imaging of water in the brain has proven very useful to establish a cartography of brain connections. It is the only in vivo modality to study anatomical connectivity. A few years ago, it has been shown that diffusion MRI is also a unique tool to perform virtual biopsy of cerebral tissues. However, most of current analytical models (AxCaliber, ActiveAx, CHARMED) employed for the estimation of white matter microstructure rely upon a basic modeling of white matter, with axons represented by simple cylinders and extra-axonal diffusion assumed to be Gaussian. First, a more physically plausible analytical model of the human brain white matter accounting for the time-dependence of the diffusion process in the extra-axonal space was developed for Oscillating Gradient Spin Echo (OGSE) sequence signals. A decoding tool enabling to solve the inverse problem of estimating the parameters of the white matter microstructure from the OGSE-weighted diffusion MRI signal was designed using a robust optimization scheme for parameter estimation. Second, a Big Data approach was designed to further improve the brain microstructure decoding. All the simulation tools necessary to construct computational models of brain tissues were developed in the frame of this thesis. An algorithm creating realistic white matter tissue numerical phantoms based on a spherical meshing of cell shapes was designed, enabling to generate a massive amount of virtual voxels in a computationally efficient way thanks to a GPU-based implementation. An ultra-fast simulation tool of the water molecules diffusion process in those virtual voxels was designed, enabling to generate synthetic diffusion MRI signal for each virtual voxel. A dictionary of virtual voxels containing a huge set of geometrical configurations present in white matter was built. This dictionary contained virtual voxels with varying degrees of axonal beading, a swelling of the axonal membrane which occurs after strokes and other pathologies. The set of synthetic signals and associated geometrical configurations of the corresponding voxels was used as a training data set for a machine learning algorithm designed to decode white matter microstructure from the diffusion MRI signal and estimate the degree of axonal beading. This decoder showed encouraging regression results on unknown simulated data, showing the potential of the presented approach to characterize the microstructure of healthy and injured brain tissues in vivo. The microstructure decoding tools developed during this thesis will in particular be used to characterize white matter tissue microstructural parameters (axonal density, mean axonal diameter, glial density, mean glial cells diameter, microvascular density ) in short and long bundles. The simulation tools developed in the frame of this thesis will enable the construction of a probabilistic atlas of the white matter bundles microstructural parameters, using a mean propagator based diffeomorphic registration tool also designed in the frame of this thesis to register each individual.
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Submitted on : Wednesday, December 18, 2019 - 10:55:13 AM
Last modification on : Tuesday, September 29, 2020 - 4:29:23 AM
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  • HAL Id : tel-02417398, version 1


Kévin Ginsburger. Modeling and simulation of the diffusion MRI signal from human brain white matter to decode its microstructure and produce an anatomic atlas at high fields (3T). Medical Imaging. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLS158⟩. ⟨tel-02417398⟩



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