Voxel-Based Analysis of High Angular Resolution Diffusion-Weighted Magnetic Resonance Images

David Raffelt (2011). Voxel-Based Analysis of High Angular Resolution Diffusion-Weighted Magnetic Resonance Images PhD Thesis, School of Information Technol and Elec Engineering, The University of Queensland.

       
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Author David Raffelt
Thesis Title Voxel-Based Analysis of High Angular Resolution Diffusion-Weighted Magnetic Resonance Images
School, Centre or Institute School of Information Technol and Elec Engineering
Institution The University of Queensland
Publication date 2011-10
Thesis type PhD Thesis
Supervisor Stuart Crozier
Olivier Salvado
Alan Connelly
J-Donald Tournier
Total pages 220
Total colour pages 45
Total black and white pages 175
Language eng
Subjects 0801 Artificial Intelligence and Image Processing
0903 Biomedical Engineering
1109 Neurosciences
Abstract/Summary Diffusion-Weighted magnetic resonance Imaging (DWI) exploits the interaction of diffusing water molecules and cellular microstructure to provide macroscopic information about the cellular organisation of oriented tissue such as white matter. White matter consists of neuronal axons that facilitate the transmission of information throughout the central nervous system. Axons are typically coherently and tightly packed, forming white matter fibre bundles. In recent years, the Diffusion Tensor model has played a prominent role in modelling the diffusion of water within white matter bundles. Rotationally invariant indices of the diffusion tensor such as fractional anisotropy (FA) have been used extensively for investigating white matter using approaches such as voxel-based analysis. However, recent evidence suggests that two or more different fibre bundles pass through each imaging voxel in up to 90\% of white matter. This complicates the interpretation of tensor-derived measures because the diffusion tensor can only model a single fibre bundle within each imaging voxel. With the introduction of High Angular Resolution Diffusion-weighted Imaging (HARDI), more advanced models of diffusion and fibre orientation have emerged that provide more information than can be represented by the diffusion tensor model. While higher-order models have been proven to benefit fibre tractography algorithms, methods to exploit this information for quantitative voxel-based analysis remain largely unexplored. In this research, a new measure called Apparent Fibre Density (AFD) is proposed for the analysis of HARDI using higher-order information provided by Fibre Orientation Distributions computed using spherical deconvolution. AFD has the potential to provide more specific information regarding differences between groups of individuals by identifying not only the location, but also the orientations along which differences exist. A number of technical developments and contributions are made in this research to enable robust voxel-based analysis of AFD. First, a novel algorithm is presented to register FOD images and provide spatial correspondence between common anatomical structures across different subjects. Then a novel approach to reorient FODs is outlined; this is an important step required during FOD spatial normalisation to ensure FOD orientations remain consistently aligned with neighbouring anatomy. Simulations are then used to support the use of the FOD amplitude as a quantitative measure (i.e. AFD) for the analysis of white matter. To perform robust voxel-based analysis of AFD, we present and evaluate a novel method to modulate the FOD to account for changes in fibre bundle cross-sectional area that occur during spatial normalisation. A novel approach for statistical analysis of AFD is then described that uses cluster-based inference of differences extended throughout space and orientation. Finally, the capability of the proposed method is demonstrated by performing voxel-based AFD comparisons between of a group of Motor Neurone Disease (MND) patients and healthy control subjects. A significant decrease in AFD was detected along voxels and orientations corresponding to both the corticospinal tract and corpus callosal fibres that connect the primary motor cortices. In addition to corroborating previous findings in MND, this research demonstrates the clear advantage of using this type of analysis by identifying differences along single fibre bundles in regions containing multiple fibre bundles.
Keyword Apparent Fibre Density
Diffusion-weighted magnetic resonance imaging
Fibre orientation distributions
Voxel-based analysis
Registration
Reorientation
Additional Notes 22,24,28,29,31,33,36,39,42,44,47,51,52,60,62,64,65,67,68,70,85,119,124,126,128,130,131,134,147,151,155,156,157,158,174,181,183,186,190,193,196,198,199,201,211

 
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Created: Mon, 23 Apr 2012, 11:15:10 EST by David Raffelt on behalf of Library - Information Access Service