Evidence map›Paper›PMID 39374133›Full record

ArticleeLife2024

Robust, fast and accurate mapping of diffusional mean kurtosis.

Megan E Farquhar, Qianqian Yang, Viktor Vegh

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Megan E FarquharSchool of Mathematical Sciences, Faculty of Science, Queensland University of Technology, Brisbane, Australia.ORCID https://orcid.org/0000-0003-0647-3637
Qianqian YangSchool of Mathematical Sciences, Faculty of Science, Queensland University of Technology, Brisbane, Australia.ORCID https://orcid.org/0000-0002-4331-5059
Viktor VeghCentre for Advanced Imaging, The University of Queensland, Brisbane, Australia.

Funding

Australian Research Council Discovery Early Career Research Award DE150101842Australian Research Council Discovery Project Award DP190101889
6 · The paper itself

Abstract

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum

Indexed as

BrainDiffusion Magnetic Resonance ImagingConnectomeHumansImage Processing, Computer-Assistedcomputational biologydiffusion MRIhigh b-valueshumanmean diffusional kurtosismultiple diffusion timesneurosciencenon-Gaussian diffusionsub-diffusion modelsystems biology

Identifiers

PMID39374133
PMCPMC11458175

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.