Diffusion magnetic resonance imaging is one of the only non-invasive imaging technique which is able to provide information on the human brain structure in-vivo.From the diffusion signal, using mathematical techniques called reconstruction models, it is possible to retrieve the probability density function of the water molecules displacement, or ensemble average propagator (EAP). From the EAP, it is possible to calculate a series of indices which provide information regarding the fiber orientation, fiber density, and the average diameter of the axons. The main aim of this thesis is the characterization of these indices, and, in particular, their validation. In order to characterize the indices, we take advantage of computer simulation of diffusion in different media, as well as human brain acquisition. In particular, we focused on the EAP indices calculated using three EAP models: the DTI, the 3D-SHORE, and the MAPMRI. The first contribution of this thesis is the developing and the comparison of the values of the indices for the different models.The second contribution of the thesis is the study of the variation of the indices with respect to the principal microstructural parameters which characterize the white matter. The third contribution of the thesis is the proposal of a new reconstruction model designed to reconstruct accurately the EAP in the case of crossing fibers. The fourth contributions is the developing of a new tensor model, which is able to capture the dependence on the timing parameters of the diffusion signal.Results show the sensibility of the EAP-derived indices to microstructural variations such as the orientation dispersion of the axons and the density of the fibers. Diameter axons variation, on the contrary, are not measurable by the EAP indices because of the slow signal decay, which would require extremely high magnetic fields to be measured. The new reconstruction models proposed provide excellent results in the modeling of crossing fibers and multiple diffusion times, respectively.
Towards Brain Tissue Microstructure Characterization using Diffusion MRI
Zucchelli, Mauro
2016
Abstract
Diffusion magnetic resonance imaging is one of the only non-invasive imaging technique which is able to provide information on the human brain structure in-vivo.From the diffusion signal, using mathematical techniques called reconstruction models, it is possible to retrieve the probability density function of the water molecules displacement, or ensemble average propagator (EAP). From the EAP, it is possible to calculate a series of indices which provide information regarding the fiber orientation, fiber density, and the average diameter of the axons. The main aim of this thesis is the characterization of these indices, and, in particular, their validation. In order to characterize the indices, we take advantage of computer simulation of diffusion in different media, as well as human brain acquisition. In particular, we focused on the EAP indices calculated using three EAP models: the DTI, the 3D-SHORE, and the MAPMRI. The first contribution of this thesis is the developing and the comparison of the values of the indices for the different models.The second contribution of the thesis is the study of the variation of the indices with respect to the principal microstructural parameters which characterize the white matter. The third contribution of the thesis is the proposal of a new reconstruction model designed to reconstruct accurately the EAP in the case of crossing fibers. The fourth contributions is the developing of a new tensor model, which is able to capture the dependence on the timing parameters of the diffusion signal.Results show the sensibility of the EAP-derived indices to microstructural variations such as the orientation dispersion of the axons and the density of the fibers. Diameter axons variation, on the contrary, are not measurable by the EAP indices because of the slow signal decay, which would require extremely high magnetic fields to be measured. The new reconstruction models proposed provide excellent results in the modeling of crossing fibers and multiple diffusion times, respectively.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/181592
URN:NBN:IT:UNIVR-181592