Diffusional kurtosis imaging: the quantification of non‐gaussian water diffusion by means of magnetic resonance imaging
Open accessAbstract
A magnetic resonance imaging method is presented for quantifying the degree to which water diffusion in biologic tissues is non-Gaussian. Since tissue structure is responsible for the deviation of water diffusion from the Gaussian behavior typically observed in homogeneous solutions, this method provides a specific measure of tissue structure, such as cellular compartments and membranes. The method is an extension of conventional diffusion-weighted imaging that requires the use of somewhat higher b values and a modified image postprocessing procedure. In addition to the diffusion coefficient, the method provides an estimate for the excess kurtosis of the diffusion displacement probability distribution, which is a dimensionless metric of the departure from a Gaussian form. From the study of six healthy adult subjects, the excess diffusional kurtosis is found to be significantly higher in white matter than in gray matter, reflecting the structural differences between these two types of cerebral tissues. Diffusional kurtosis imaging is related to q-space imaging methods, but is less demanding in terms of imaging time, hardware requirements, and postprocessing effort. It may be useful for assessing tissue structure abnormalities associated with a variety of neuropathologies.
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DKI extends DTI by quantifying non-Gaussian diffusion and is therefore sensitive to microstructural heterogeneity and restricted diffusion arising from complex tissue architecture.
“DKI extends DTI by quantifying non‐Gaussian diffusion and is therefore sensitive to microstructural heterogeneity and restricted diffusion arising from complex tissue architecture.”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [25]
The Conclusion describes DKI as quantifying non-Gaussian diffusion to provide a sensitive measure of tissue structure, and the Discussion links kurtosis to structural complexity and diffusion barriers. But the paper calls DKI an extension of diffusion-weighted imaging (DWI), not diffusion tensor imaging (DTI); it discusses DTI as a distinct technique with different sensitivities. No page numbers are available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredNon-Gaussian diffusion is typically a consequence of barriers and compartments, and higher kurtosis values imply greater diffusional heterogeneity.
“Non-Gaussian diffusion is typically a consequence of barriers and compartments, and higher kurtosis values imply greater diffusional heterogeneity [12–14].”
From Brain microstructure abnormalities in the 3xTg-AD mouse – A diffusion MRI and morphology correlation study (2022) · cites this paper as [12]
The Special Cases section says kurtosis reflects diffusional heterogeneity in a compartment model and tends to increase with heterogeneity; it also says diffusion barriers and water exchange can alter kurtosis. The claim overstates this qualified relationship by implying that a higher kurtosis value necessarily means greater heterogeneity. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDiffusion kurtosis imaging was among the earliest MRI approaches developed to describe empirically the curvature of the diffusion-weighted MRI signal decay with b-value, reflecting the underlying non-Gaussian displacement probability distribution.
“Diffusion kurtosis imaging (DKI) was among the earliest MRI approaches developed to describe empirically the curvature of the diffusion-weighted MRI signal decay with b-value, reflecting the underlying non-Gaussian displacement probability distribution (Jensen et al., 2005).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Jensen et al., 2005
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“Diffusion kurtosis imaging (DKI) was among the earliest MRI approaches developed to describe empirically the curvature of the diffusion-weighted MRI signal decay with b-value, reflecting the underlying non-Gaussian displacement probability distribution ( Jensen et al., 2005).”
- Authors
- Jensen, J. H., Helpern, J. A., Ramani, A., Lu, H., & Kaczynski, K.
- Affiliations
- NYU Langone Health (US)
- New York University (US)
- Siemens (Germany) (DE)
- Corresponding author
- Jensen, Jens H.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2005-05-19
- Volume
- 53
- Issue
- 6
- Pages
- 1432-1440
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2116823839
- Cited by
- 2,488
- References
- 16
- Citations by year
- 2026: 129 · 2025: 174 · 2024: 195 · 2023: 178 · 2022: 194 · 2021: 218 · 2020: 180 · 2019: 191 · 2018: 161 · 2017: 176 · 2016: 154 · 2015: 126 · 2014: 112 · 2013: 91 · 2012: 50
- FWCI
- 10.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Magnetic Resonance in Medicine (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 kurtosis imaging, excess kurtosis, diffusion-weighted imaging, q-space imaging, white matter, gray matter, cell compartments, diffusion coefficient, neuropathology
- MeSH terms
- Body Water, Brain Mapping, Diffusion, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Anisotropy, Phantoms, Imaging
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