ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation

Callaghan, Ross, Alexander, Daniel C., Palombo, Marco ORCID: https://orcid.org/0000-0003-4892-7967 and Zhang, Hui 2020. ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation. NeuroImage 220 , 117107. 10.1016/j.neuroimage.2020.117107

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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-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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Additional Information:	This is an open access article under the terms of the CC-BY Attribution 4.0 International license.
Publisher:	Elsevier
ISSN:	1053-8119
Date of First Compliant Deposit:	3 March 2022
Date of Acceptance:	25 June 2020
Last Modified:	02 May 2023 19:02
URI:	https://orca.cardiff.ac.uk/id/eprint/147896

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