3 research outputs found
Histological validation of the brain cell body imaging with diffusion MRI at ultrahigh field
Biophysical modelling of diffusion-weighted MRI (DW-MRI) data can help to gain more insight into brain microstructure. However, models need to be validated. This work validates a recently-developed technique for non-invasive mapping of brain cell-body (soma) size/ density with DW-MRI, by using ultrahigh-field DW-MRI experiments and histology of mouse brain. Predictions from numerical simulations are experimentally confirmed and brain’s maps of MR-measured soma size/density are shown to correspond very well with histology. We provide differential contrasts between cell layers that are less expressed in tensor analyses, leading to novel complementary contrasts of the brain tissue. Limitations and future research directions are discussed
Histological validation of the brain cell body imaging with diffusion MRI at ultrahigh field
Biophysical modelling of diffusion-weighted MRI (DW-MRI) data can help to gain more insight into brain microstructure. However, models need to be validated. This work validates a recently-developed technique for non-invasive mapping of brain cell-body (soma) size/ density with DW-MRI, by using ultrahigh-field DW-MRI experiments and histology of mouse brain. Predictions from numerical simulations are experimentally confirmed and brain’s maps of MR-measured soma size/density are shown to correspond very well with histology. We provide differential contrasts between cell layers that are less expressed in tensor analyses, leading to novel complementary contrasts of the brain tissue. Limitations and future research directions are discussed
ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation
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