In silico studies of diffusion MRI are becoming a standard
tool for testing the sensitivity of the technique to changes in white matter (WM)
structures. To perform such simulations, realistic models of brain tissue
microstructure are needed. However, most of the computational results are
obtained considering straight and parallel cylinders models, which are known to
be too simplistic for representing real-scenario situations. We present a
statistical-driven approach for obtaining random models of WM tissue samples
based on histomorphometric data available in the literature. We show the
versatility of the method for characterising WM voxels representing bundles and
disordered structures.
