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Microstructural and Physiological Features of Tissues Elucidated by Quantitative-Diffusion-Tensor MRI
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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In the citing paper
“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).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Basser and Pierpaoli, 1996
In the citing paper
“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).”
From The role of myelination in measures of white matter integrity: Combination of diffusion tensor imaging and two-photon microscopy of CLARITY intact brains (2017) · cites this paper as Basser and Pierpaoli, 1996
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Metadata
- 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 › Chemistry › Spectroscopy › 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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