Decoding the microstructural properties of white matter using realistic models

Data Sharing collection allowing to reproduce the results in the publication: Hédouin, R., Metere, R., Chan, K.-S., Licht, C., Mollink, J., van Walsum, A.-M.C., Marques, J.P., Decoding the microstructural properties of white matter using realistic models, (2021) NeuroImage, 237, art. no. 118138, DOI: 10.1016/j.neuroimage.2021.118138 Highlights - A pipeline to generate realistic white models of arbitrary fiber volume fraction and g-ratio is provided. - Code to simulated the gradient echo signal from segmented 2D and 3D models of white matter, which takes into account the interaction of the static magnetic field with the anisotropic susceptibility of the myelin phospholipids using a new compartmentalization model within the myelin sheath. - Code for training and using Deep Learning Networks that can be used to decode microstructural white matter parameters from the signal of multi-echo multi-orientation data. - Multi-echo gradient data of an ex-vivo Brain sample acquired at 3T with different flip angles and multiple orientations of the sample in respect to the static magnetic field.