Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation

Abstract

This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-byone, 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.

Axon morphology is modulated by the local environment and impacts the non-invasive investigation of its structure-function relationship

“…Still, EM is time demanding and there is a need to combine techniques that bridge different resolutions and volumes; it would be valuable to image a sample by synchrotron XNH, and thereafter image a sub-region of the same sample with 3D EM.Lastly, the axonal trajectory variations and dispersion behavior presented here could act as an axonal "fingerprint" to guide the construction of anatomically informed axonal phantoms for MC simulations. Existing frameworks have been developed to model morphological features such as fiber undulation43,51 (although we do not observe periodic undulations in our data), and diameter variations52,53 . Others allow for the generation of a more complex WM environment with beaded axons and cells54 .…”

Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination

“…Due to current limitations in our simulation system, we make several assumptions about the geometry of the tissue such as representing axons as non-abutting parallel cylinders. Future work should aim to train the machine learning model on more realistic simulations, which account for different effects such as myelin water (Harkins andDoes, 2016, Brusini et al, 2019), axonal undulation (Nilsson et al, 2012), dispersion (Ginsburger et al, 2019, Ross Callaghan, 2019, neurons and glial cells (Palombo et al, 2018). Such effects, once included in the simulations, can easily be incorporated in the machine learning framework used in this paper.…”