This study presents the creation of 2D white matter models, based on real histologically derived axon shapes, with large range of microstructure parameters (FVF, g-ratio). These models are used to simulate the complex gradient echo signal evolution under different main magnetic field orientations for (amongst other parameters) varying magnetic susceptibility and water density in the myelin compartment. A deep learning network, trained from those data, shows its capacity to recover parameter microstructure properties as g-factor and susceptibility on test data.
