Jens H. Jensen
Medical University of South Carolina
200 Papers
1.1K Citations
Jens H. Jensen is an academic researcher from Medical University of South Carolina. The author has contributed to research in topics: Diffusion MRI & Kurtosis. The author has an hindex of 46, co-authored 188 publications. Previous affiliations of Jens H. Jensen include New York University & Sewanee: The University of the South.
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Papers
Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging
TL;DR: 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.
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MRI quantification of non-Gaussian water diffusion by kurtosis analysis.
Jens H. Jensen,Joseph A. Helpern +1 more
TL;DR: It is argued that the diffusional kurtosis is sensitive to diffusional heterogeneity and suggested that DKI may be useful for investigating ischemic stroke and neuropathologies, such as Alzheimer's disease and schizophrenia.
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White matter characterization with diffusional kurtosis imaging.
TL;DR: A physically meaningful interpretation of DKI metrics in white matter regions consisting of more or less parallel aligned fiber bundles is provided by modeling the tissue as two non-exchanging compartments, the intra-axonal space and extra-AXonal space.
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Estimation of Tensors and Tensor-Derived Measures in Diffusional Kurtosis Imaging
TL;DR: Two related advancements to the diffusional kurtosis imaging estimation framework are presented to increase its robustness to noise, motion, and imaging artifacts and increase the efficiency and accuracy of the estimation of mean and radial kurtoses by applying exact closed‐form formulae.
Revealing mesoscopic structural universality with diffusion.
TL;DR: The dynamical exponent in the time dependence of the diffusion coefficient distinguishes between the universality classes of the mesoscopic structural complexity, which enables the interpretation of diffusion measurements by objectively selecting and modeling the most relevant structural features.