  This paper presents Contextual Fibre Growth (ConFiG), an approach to generate
white matter numerical phantoms by mimicking natural fibre genesis. ConFiG
grows fibres one-by-one, following simple rules motivated by real axonal
guidance mechanisms. These simple rules enable ConFiG to generate phantoms with
tuneable microstructural features by growing fibres while attempting to meet
morphological targets such as user-specified density and orientation
distribution. We compare ConFiG to the state-of-the-art approach based on
packing fibres together by generating phantoms in a range of fibre
configurations including crossing fibre bundles and orientation dispersion.
Results demonstrate that ConFiG produces phantoms with up to 20% higher
densities than the state-of-the-art, particularly in complex configurations
with crossing fibres. We additionally show that the microstructural morphology
of ConFiG phantoms is comparable to real tissue, producing diameter and
orientation distributions close to electron microscopy estimates from real
tissue as well as capturing complex fibre cross sections. Signals simulated
from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG
phantoms can be used to generate realistic diffusion MRI data. This
demonstrates the feasibility of ConFiG to generate realistic synthetic
diffusion MRI data for developing and validating microstructure modelling
approaches.

Medical Physics;Quantitative Methods