Diffusion tensor imaging: Concepts and applications
Abstract
The success of diffusion magnetic resonance imaging (MRI) is deeply rooted in the powerful concept that during their random, diffusion‐driven displacements molecules probe tissue structure at a microscopic scale well beyond the usual image resolution. As diffusion is truly a three‐dimensional process, molecular mobility in tissues may be anisotropic, as in brain white matter. With diffusion tensor imaging (DTI), diffusion anisotropy effects can be fully extracted, characterized, and exploited, providing even more exquisite details on tissue microstructure. The most advanced application is certainly that of fiber tracking in the brain, which, in combination with functional MRI, might open a window on the important issue of connectivity. DTI has also been used to demonstrate subtle abnormalities in a variety of diseases (including stroke, multiple sclerosis, dyslexia, and schizophrenia) and is currently becoming part of many routine clinical protocols. The aim of this article is to review the concepts behind DTI and to present potential applications. J. Magn. Reson. Imaging 2001;13:534–546. © 2001 Wiley‐Liss, Inc.
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Even where more sophisticated models are available, DTI often remains the first analytic layer because it provides a stable orientation field and familiar summary indices.
“Even where more sophisticated models are available, DTI often remains the first analytic layer because it provides a stable orientation field and a familiar set of summary indices (Le Bihan et al., 2001).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Le Bihan et al., 2001
Noise floor effects, Gibbs ringing, eddy currents, susceptibility distortions, and free-water partial volume can bias tensor estimates unless explicitly controlled.
“Noise floor effects, Gibbs ringing, eddy currents, susceptibility distortions, and free-water partial volume can all bias tensor estimates unless explicitly controlled (Beaulieu, 2002; Le Bihan et al., 2001; Tournier et al., 2011).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Le Bihan et al., 2001
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).”
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 Le Bihan et al., 2001
Diffusion tensor imaging is a magnetic resonance imaging technique that estimates white matter in vivo.
“Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that estimates white matter in vivo (Bihan et al., 2001) and is increasingly being used to study the biology of addiction.”
From Substance abuse and white matter: Findings, limitations, and future of diffusion tensor imaging research (2019) · cites this paper as Bihan et al., 2001
DTI is an MRI method that estimates white matter in the living human brain.
“Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that estimates white matter in vivo (Bihan et al., 2001)”
From Substance abuse and white matter: Findings, limitations, and future of diffusion tensor imaging research (2019) · cites this paper as Bihan et al., 2001
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40 Years of diffusion MRI in the brain: From history to emerging frontiers
Le Bihan, D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1365
“Even where more sophisticated models are available, DTI often remains the first analytic layer because it provides a stable orientation field and a familiar set of summary indices ( Le Bihan et al., 2001).”
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- Authors
- Le Bihan, D., Mangin, J., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Venue
- Journal of Magnetic Resonance Imaging
- Publisher
- Wiley
- Published
- 2001-03-21
- Volume
- 13
- Issue
- 4
- Pages
- 534-546
- ISSN
- 1053-1807 (ISSN-L)
- 1522-2586
- OpenAlex
- W2120259577
- Cited by
- 3,547
- References
- 128
- Citations by year
- 2026: 89 · 2025: 131 · 2024: 139 · 2023: 136 · 2022: 135 · 2021: 182 · 2020: 157 · 2019: 145 · 2018: 159 · 2017: 179 · 2016: 190 · 2015: 173 · 2014: 211 · 2013: 179 · 2012: 181
- FWCI
- 7.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- 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 tensor imaging, diffusion MRI, diffusion anisotropy, white matter microstructure, tractography, brain connectivity, functional MRI, stroke, multiple sclerosis, dyslexia, schizophrenia, clinical application
- MeSH terms
- Brain Diseases, Diffusion, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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