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A Versatile Murine Model of Subcortical White Matter Stroke for the Study of Axonal Degeneration and White Matter Neurobiology

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White Matter Tissue Quantification at Low

Alessandro Calamuneri¹, Alessandro Arrigo², Enricomaria Mormina^3,4

¹IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy.

Frontiers in Neurology

|September 14, 2018

Summary

This summary is machine-generated.

Constrained Spherical Deconvolution (CSD) and Diffusion Tensor Imaging (DTI) were compared using low-demand brain imaging data. CSD demonstrated superior performance and Apparent Fiber Density (AFD) remains a meaningful measure for intra-axonal volume in clinical settings.

Keywords:

AFD CSD DTI arcuate fasciculus corticospinal tract diffusion MRI tractography white matter quantification

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Area of Science:

Neuroimaging
Diffusion Weighted Imaging (DWI)
White Matter (WM) analysis

Background:

Diffusion Weighted Imaging (DWI) techniques non-invasively study brain white matter (WM).
Constrained Spherical Deconvolution (CSD) accurately characterizes water diffusion but requires high-resolution data (b-values ~3,000 s/mm², ≥45 directions).
Diffusion Tensor Imaging (DTI) is widely used but cannot resolve complex fiber geometries, impacting quantification.

Purpose of the Study:

To compare Constrained Spherical Deconvolution (CSD) and Diffusion Tensor Imaging (DTI) outcomes on low-demand MR-DW datasets.
To evaluate the interpretability and accuracy of Apparent Fiber Density (AFD) in a clinically relevant, low-resolution setting.
To assess the impact of diffusion models and thresholding on WM quantification using tractography.

Main Methods:

Performed deterministic and probabilistic tractographic reconstruction of the Corticospinal Tract and Arcuate Fasciculus.
Utilized low-demand MR-DW datasets (single shell, b-value = 1,000 s/mm², 30 directions).
Estimated and analyzed DTI-based features and Apparent Fiber Density (AFD), employing multivariate statistics and tract-based ROI analysis.

Main Results:

Constrained Spherical Deconvolution (CSD) outperformed Diffusion Tensor Imaging (DTI) even with limited data.
WM quantification was significantly affected by the chosen diffusion model and thresholding on noisy tractographic maps.
Apparent Fiber Density (AFD) proved to be a biologically meaningful measure for intra-axonal volume analysis in low-demand DW imaging.

Conclusions:

CSD provides more accurate WM quantification than DTI, even under standard clinical imaging constraints.
Apparent Fiber Density (AFD) is a valuable and interpretable metric for intra-axonal volume estimation in low-resolution clinical DWI.
This study validates CSD and AFD for clinical applications, enabling more precise WM analysis with accessible imaging protocols.