Modelling white matter microstructure using diffusion OGSE MRI: Model and analysis choices

Alterations in white matter (WM) microstructure of the central nervous system have been shown to be pathophysiological presentations of various neurodegenerative disorders. Current methods for measuring such WM features require ex vivo tissue samples analyzed using electron microscopy. Magnetic Resonance Imaging (MRI) diffusion-weighted pulse sequences provide a non-invasive tool for estimating such microstructural features in vivo. The current project investigated the use of two methods of analysis, including the ROI-based (Region of Interest, RBA) and voxel-based analysis (VBA), as well as four mathematical models of WM microstructure, including the ActiveAx Frequency-Independent Extra-Axonal Diffusion (AAI), ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD), AxCaliber Frequency-Independent Extra-Axonal Diffusion (ACI), and AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) models. Two mice samples imaged at 7 T and 15.2 T were analyzed. Both the AAI and AAD models provide a single value for each of the fit parameters, including mean effective axon diameter AxD¯, packing fraction f<sub>in</sub>, intra-cellular and D<sub>in</sub> and extra-cellular D<sub>ex</sub> diffusion coefficients, as well as the frequency dependence of D<sub>ex</sub>, β<sub>ex</sub> for the AAD model. The ACI and ACD models provide this, in addition to a distribution of axon diameters for a chosen ROI. VBA extends this, providing a parameter value for each voxel within the selected ROI, at the cost of increased computational load and analysis time. Overall, RBA-ACD and VBA-AAD were found to be optimal for parameter fitting to physically relevant values in a reasonable time frame. A full comparison of each combination of RBA and VBA with AAI, AAD, ACI, and ACD is provided to give the reader sufficient information to make an informed decision of which model is best for their own experiments.

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Publication Details

Journal
Magnetic Resonance Imaging
Published
2024-08-22
DOI
https://doi.org/10.1016/j.mri.2024.110221
Citations
1
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.40
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Modelling white matter microstructure using diffusion OGSE MRI: Model and analysis choices

Emma L. Friesen, John C. Gore, Melanie Martin, Bibek Dhakal et al.
1 citations
Magnetic Resonance Imaging
Advanced Neuroimaging Techniques and Applications
0.40
article

Modelling white matter microstructure using diffusion OGSE MRI: Model and analysis choices

Emma L. Friesen, John C. Gore, Melanie Martin, Bibek Dhakal, Morgan Mercredi, Mark D. Does, Madison Chisholm
article en
1 citations

Abstract

Alterations in white matter (WM) microstructure of the central nervous system have been shown to be pathophysiological presentations of various neurodegenerative disorders. Current methods for measuring such WM features require ex vivo tissue samples analyzed using electron microscopy. Magnetic Resonance Imaging (MRI) diffusion-weighted pulse sequences provide a non-invasive tool for estimating such microstructural features in vivo. The current project investigated the use of two methods of analysis, including the ROI-based (Region of Interest, RBA) and voxel-based analysis (VBA), as well as four mathematical models of WM microstructure, including the ActiveAx Frequency-Independent Extra-Axonal Diffusion (AAI), ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD), AxCaliber Frequency-Independent Extra-Axonal Diffusion (ACI), and AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) models. Two mice samples imaged at 7 T and 15.2 T were analyzed. Both the AAI and AAD models provide a single value for each of the fit parameters, including mean effective axon diameter AxD¯, packing fraction f<sub>in</sub>, intra-cellular and D<sub>in</sub> and extra-cellular D<sub>ex</sub> diffusion coefficients, as well as the frequency dependence of D<sub>ex</sub>, β<sub>ex</sub> for the AAD model. The ACI and ACD models provide this, in addition to a distribution of axon diameters for a chosen ROI. VBA extends this, providing a parameter value for each voxel within the selected ROI, at the cost of increased computational load and analysis time. Overall, RBA-ACD and VBA-AAD were found to be optimal for parameter fitting to physically relevant values in a reasonable time frame. A full comparison of each combination of RBA and VBA with AAI, AAD, ACI, and ACD is provided to give the reader sufficient information to make an informed decision of which model is best for their own experiments.

Magnetic Resonance ImagingVol. 113
The University of Winnipeg (CA)
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Advanced Neuroimaging Techniques and Applications
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