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Related Experiment Video

Updated: Feb 11, 2026

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Modeling white matter microstructure with fiber ball imaging.

Emilie T McKinnon1, Joseph A Helpern2, Jens H Jensen3

  • 1Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neurology, Medical University of South Carolina, Charleston, SC, USA; Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.

Neuroimage
|April 17, 2018
PubMed
Summary
This summary is machine-generated.

Fiber ball imaging (FBI) models white matter microstructure using diffusion MRI (dMRI). The fiber ball white matter (FBWM) method enhances accuracy for axonal water fraction and intra-axonal diffusivity, offering computational advantages.

Keywords:
Axonal water fractionBrain microstructureDiffusion MRIFiber ballIntra-axonal diffusivityWhite matter tract integrity

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Computational Neuroscience

Background:

  • Diffusion MRI (dMRI) is crucial for white matter analysis.
  • Fiber ball imaging (FBI) calculates fiber orientation density functions (fODFs).
  • Accurate microstructural parameter estimation remains a challenge.

Purpose of the Study:

  • To introduce and validate the fiber ball white matter (FBWM) modeling method.
  • To compare FBWM with the white matter tract integrity (WMTI) method.
  • To assess the accuracy of estimated microstructural parameters.

Main Methods:

  • Utilizing dMRI data from high b-value shells (≥4000 s/mm²) for FBI-derived fODFs.
  • Supplementing with dMRI data from two lower b-value shells.
  • Estimating axonal water fraction (AWF) and intra-axonal diffusivity using FBWM.

Main Results:

  • FBWM yielded significantly larger AWF and intra-axonal diffusivity compared to WMTI.
  • FBWM's intra-axonal diffusivity aligns with isotropic diffusion weighting results.
  • FBWM requires fitting a cost function with only one free parameter, simplifying computation.

Conclusions:

  • FBWM is a robust and efficient method for white matter microstructural modeling.
  • FBWM provides more accurate estimations of key microstructural parameters.
  • The simplified computational approach of FBWM facilitates its practical implementation.