Perhaps more than any other “-omics” endeavor, the accuracy and level of detail obtained
from map** the major connection pathways in the living human brain with diffusion MRI …

Purpose We introduce L2‐regularized reconstruction algorithms with closed‐form solutions
that achieve dramatic computational speed‐up relative to state of the art L1‐and L2‐based …

Purpose Diffusion Spectrum Imaging enables to reconstruct the ensemble average
propagator (EAP) at the expense of having to acquire a large number of measurements …

While many useful microstructural indices, as well as orientation distribution functions, can
be obtained from multi-shell dMRI data, there is growing interest in exploring the richer set of …

In this work, we first propose an original and efficient computational framework to model
continuous diffusion MRI (dMRI) signals and analytically recover important diffusion features …

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of
substantially long imaging times. It is possible to accelerate acquisition by undersampling in …

This thesis is dedicated to the development of new acquisition and processing methods in
diffusion MRI (dMRI) to characterize the diffusion of water molecules in white matter fiber …

The emergence of sparse reconstruction methods for undersampled data in Magnetic
Resonance Imaging (MRI), such as Compressed Sensing (CS), have been valuable tools to …

The objective of this thesis is to provide a complete pipeline that achieves an accurate
reconstruction of the white matter fascicles using clinical diffusion images characterized by a …

Compressive Sensing (CS)[2],[1] offers an efficient way to decrease the number of
measurements required in Diffusion Spectrum Imaging (DSI). This method aims to …