Cited by 4,306Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsNMR spectroscopy and applications

Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI

Basser, Peter J., Pierpaoli, Carlo·1996·Journal of Magnetic Resonance, Series B

Abstract

Quantitative-diffusion-tensor MRI consists of deriving and displaying parameters that resemble histological or physiological stains, i.e., that characterize intrinsic features of tissue microstructure and microdynamics. Specifically, these parameters are objective, and insensitive to the choice of laboratory coordinate system. Here, these two properties are used to derive intravoxel measures of diffusion isotropy and the degree of diffusion anisotropy, as well as intervoxel measures of structural similarity, and fiber-tract organization from the effective diffusion tensor, D, which is estimated in each voxel. First, D is decomposed into its isotropic and anisotropic parts, [D] I and D - [D] I, respectively (where [D] = Trace(D)/3 is the mean diffusivity, and I is the identity tensor). Then, the tensor (dot) product operator is used to generate a family of new rotationally and translationally invariant quantities. Finally, maps of these quantitative parameters are produced from high-resolution diffusion tensor images (in which D is estimated in each voxel from a series of 2D-FT spin-echo diffusion-weighted images) in living cat brain. Due to the high inherent sensitivity of these parameters to changes in tissue architecture (i.e., macromolecular, cellular, tissue, and organ structure) and in its physiologic state, their potential applications include monitoring structural changes in development, aging, and disease.

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Registry

Crossref · 10.1006/jmrb.1996.0086

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  • Popularly used diffusion-modeling-based microstructural measures include FA, AD, RD and MD derived from the traditional diffusion tensor model.

    “Popularly used diffusion-modeling-based microstructural measures include the fractional anisotropy, axial, radial and mean diffusivities (FA, AD, RD and MD respectively) derived from traditional diffusion tensor model (Basser and Pierpaoli, 1996).”
  • DTI is the most commonly employed diffusion model and utilizes the magnitude and orientation of anisotropic water diffusion to estimate the WM structure of brain tissue, producing maps of macroscopic axonal organization.

    “Diffusion tensor imaging (DTI) is the most commonly employed diffusion model and utilizes the magnitude and orientation of anisotropic water diffusion to estimate the WM structure of brain tissue, thereby producing maps of macroscopic axonal organization in the brain (Basser and Pierpaoli, 1996; Le Bihan et al., 2001; Mori and Zhang, 2006).”

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Authors
Basser, P. J., & Pierpaoli, C.
Affiliations
  • National Institutes of Health (US)
  • National Institute of Neurological Disorders and Stroke (US)
Venue
Journal of Magnetic Resonance, Series B
Publisher
Elsevier BV
Published
1996-06
Volume
111
Issue
3
Pages
209-219
ISSN
1064-1866 (ISSN-L)
OpenAlex
W2139158372
Cited by
4,306
References
1
Citations by year
2026: 47 · 2025: 91 · 2024: 100 · 2023: 101 · 2022: 122 · 2021: 161 · 2020: 147 · 2019: 188 · 2018: 160 · 2017: 180 · 2016: 188 · 2015: 262 · 2014: 234 · 2013: 252 · 2012: 264
FWCI
30.98
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Magnetic Resonance Series B (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
Keywords
diffusion anisotropy, structural similarity, mean diffusivity, tensor decomposition, cat brain, tissue microstructure, diffusion-weighted imaging, structural neuroimaging
MeSH terms
Animals, Brain, Cats, Diffusion, Electron Spin Resonance Spectroscopy, Fourier Analysis, Image Enhancement, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Sensitivity and Specificity

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