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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Multidimensional diffusion MRI.

Daniel Topgaard1

  • 1Physical Chemistry, Lund University, P.O.B. 124, SE-22100 Lund, Sweden.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|January 2, 2017
PubMed
Summary
This summary is machine-generated.

Solid-state NMR principles enhance diffusion MRI for detailed brain microstructure analysis. New methods quantify tissue composition using diffusion tensor distributions, revealing features like cell shape and density.

Keywords:
KurtosisMagic-angle spinningMagnetic resonanceModulated gradientsPulsed gradient spin echob-Tensorq-Vector

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Area of Science:

  • Biophysics
  • Neuroimaging
  • Materials Science

Background:

  • Multidimensional NMR spectroscopy, particularly solid-state NMR, offers detailed material characterization.
  • Diffusion MRI provides non-invasive insights into heterogeneous anisotropic materials like the human brain.

Purpose of the Study:

  • To explore the physics behind the analogy between solid-state NMR and diffusion MRI.
  • To review current diffusion MRI methods for quantifying material composition.
  • To detail how diffusion tensor distributions reveal microstructural properties.

Main Methods:

  • Revisiting the fundamental physics of solid-state NMR and diffusion MRI.
  • Overview of current diffusion MRI acquisition protocols and data analysis.
  • Quantifying material composition using diffusion tensor distributions (size, shape, orientation).

Main Results:

  • Advanced methods estimate complete multidimensional diffusion distributions.
  • Simpler methods use projections and calculate means/variances.
  • Scalar parameters relate to microstructural features like cell eccentricity and density variance.

Conclusions:

  • Diffusion MRI, inspired by solid-state NMR, allows unprecedented characterization of brain microstructure.
  • Combining isotropic and directional MRI signal acquisition is key to disentangling microstructural features.
  • Methods provide intuitive scalar parameters linked to microscopic properties.