Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/16327
Title: Investigating white matter fibre density and morphology using fixel-based analysis
Authors: Raffelt, David A;Tournier, Jacques-Donald;Smith, Robert E;Vaughan, David N;Jackson, Graeme D;Ridgway, Gerard R;Connelly, Alan
Issue Date: 14-Sep-2016
EDate: 2016-09-14
Citation: NeuroImage 2016; online first: 14 September
Abstract: Voxel-based analysis of diffusion MRI data is increasingly popular. However, most white matter voxels contain contributions from multiple fibre populations (often referred to as crossing fibres), and therefore voxel-averaged quantitative measures (e.g. fractional anisotropy) are not fibre-specific and have poor interpretability. Using higher-order diffusion models, parameters related to fibre density can be extracted for individual fibre populations within each voxel ('fixels'), and recent advances in statistics enable the multi-subject analysis of such data. However, investigating within-voxel microscopic fibre density alone does not account for macroscopic differences in the white matter morphology (e.g. the calibre of a fibre bundle). In this work, we introduce a novel method to investigate the latter, which we call fixel-based morphometry (FBM). To obtain a more complete measure related to the total number of white matter axons, information from both within-voxel microscopic fibre density and macroscopic morphology must be combined. We therefore present the FBM method as an integral piece within a comprehensive fixel-based analysis framework to investigate measures of fibre density, fibre-bundle morphology (cross-section), and a combined measure of fibre density and cross-section. We performed simulations to demonstrate the proposed measures using various transformations of a numerical fibre bundle phantom. Finally, we provide an example of such an analysis by comparing a clinical patient group to a healthy control group, which demonstrates that all three measures provide distinct and complementary information. By capturing information from both sources, the combined fibre density and cross-section measure is likely to be more sensitive to certain pathologies and more directly interpretable.
URI: http://ahro.austin.org.au/austinjspui/handle/1/16327
DOI: 10.1016/j.neuroimage.2016.09.029
PubMed URL: http://www.ncbi.nlm.nih.gov/pubmed/27639350
Type: Journal Article
Subjects: Diffusion
Fibre
Fixel
Magnetic Resonance Imaging
Appears in Collections:Journal articles

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