Evidence map›Paper›PMID 25728763›Full record

ArticleNMR in biomedicine2015

Quantitative assessment of diffusional kurtosis anisotropy.

G Russell Glenn, Joseph A Helpern, Ali Tabesh, Jens H Jensen

Abstract readComparative Study
In one paragraph

Article in NMR in biomedicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

76 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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16 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

G Russell GlennCenter for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neurosciences, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
Joseph A Helpern
Ali Tabesh
Jens H Jensen

Funding

TRAINING PROGRAM FOR MEDICAL SCIENTISTST32GM008716 · NIGMS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI DEMORE, NANCY · 1999 to 2024
$9.3M
NIGMS NIH HHS T32 GM008716NIGMS NIH HHS T32GM008716
6 · The paper itself

Abstract

Diffusional kurtosis imaging (DKI) measures the diffusion and kurtosis tensors to quantify restricted, non-Gaussian diffusion that occurs in biological tissue. By estimating the kurtosis tensor, DKI accounts for higher order diffusion dynamics, when compared with diffusion tensor imaging (DTI), and consequently can describe more complex diffusion profiles. Here, we compare several measures of diffusional anisotropy which incorporate information from the kurtosis tensor, including kurtosis fractional anisotropy (KFA) and generalized fractional anisotropy (GFA), with the diffusion tensor-derived fractional anisotropy (FA). KFA and GFA demonstrate a net enhancement relative to FA when multiple white matter fiber bundle orientations are present in both simulated and human data. In addition, KFA shows net enhancement in deep brain structures, such as the thalamus and the lenticular nucleus, where FA indicates low anisotropy. Thus, KFA and GFA provide additional information relative to FA with regard to diffusional anisotropy, and may be particularly advantageous for the assessment of diffusion in complex tissue environments.

Indexed as

AdultAlgorithmsAnisotropyDatasets as TopicDiffusion Magnetic Resonance ImagingDiffusion Tensor ImagingFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedNormal DistributionWhite MatteranisotropydiffusionDKIFAGFAKFAkurtosisnon-Gaussian

Identifiers

PMID25728763
PMCPMC4378654

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.