Numerical phantoms have played a key role in the development of diffusion MRI (dMRI)
techniques seeking to estimate features of the microscopic structure of tissue by providing
a ground truth for simulation experiments against which we can validate and compare
techniques. One common limitation of numerical phantoms which represent white matter
(WM) is that they oversimplify the true complex morphology of the tissue which has
been revealed through ex vivo studies. It is important to try to generate WM numerical
phantoms that capture this realistic complexity in order to understand how it impacts the
dMRI signal.
This thesis presents work towards improving the realism of WM numerical phantoms
by generating fibres mimicking natural fibre genesis. A novel phantom generator is
presented which was developed over two works, resulting in Contextual Fibre Growth
(ConFiG). 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. ConFiG is applied to investigate the intra-axonal diffusivity
and probe assumptions in a family of dMRI modelling techniques based on spherical
deconvolution (SD), demonstrating that the microscopic variations in fibres’ shapes
affects the diffusion within axons. This leads to variations in the per-fibre signal contrary
to the assumptions inherent in SD which may have a knock-on effect in popular techniques
such as tractography
