Evidence map›Paper›PMID 32080890›Full record

ArticleMagnetic resonance in medicine2020

Direction-averaged diffusion-weighted MRI signal using different axisymmetric B-tensor encoding schemes.

Maryam Afzali, Santiago Aja-Fernández, Derek K Jones

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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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.

Maryam AfzaliCardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.ORCID 0000-0003-3378-0878
Santiago Aja-FernándezCardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
Derek K JonesCardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.

Funding

Wellcome TrustWellcome Trust 096646/Z/11/ZWellcome Trust 104943/Z/14/Z
6 · The paper itself

Abstract

purposeIt has been shown, theoretically and in vivo, that using the Stejskal-Tanner pulsed-gradient, or linear tensor encoding (LTE), and in tissue exhibiting a "stick-like" diffusion geometry, the direction-averaged diffusion-weighted MRI signal at high b-values (

methodsWe considered the signal decay for high b-values for encoding geometries ranging from 2-dimensional PTE, through isotropic or spherical tensor encoding to LTE. When a power-law behavior was suggested, this was tested using in silico simulations and, when appropriate, in vivo using ultra-strong (300 mT/m) gradients.

resultsOur in vivo results confirmed the predicted 1/b power law for PTE. Moreover, our analysis showed that using an axisymmetric b-tensor a power-law only exists under very specific conditions: (a) "stick-like" tissue geometry and purely LTE or purely PTE waveforms; and (b) "pancake-like" tissue geometry and a purely LTE waveform.

conclusionsA complete analysis of the power-law dependencies of the diffusion-weighted signal at high b-values has been performed. Only three specific forms of encoding result in a power-law dependency, pure linear and pure PTE when the tissue geometry is "stick-like" and pure LTE when the tissue geometry is "pancake-like". The different exponents of these encodings could be used to provide independent validation of the presence of different tissue geometries in vivo.

Indexed as

Diffusion Magnetic Resonance ImagingDiffusion Tensor ImagingComputer SimulationDiffusionB-tensor encodingdiffusion-weighted MRIdirection-averaged diffusion signalhigh b-valuepower-law

Identifiers

PMID32080890
PMCPMC7318161

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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.