Evidence map›Paper›PMID 32492059›Full record

ArticlePloS one2020

Quantification of cerebral veins in patients with acute migraine with aura: A fully automated quantification algorithm using susceptibility-weighted imaging.

Philipe Sebastian Breiding, Frauke Kellner-Weldon, Lorenz Grunder, Adrian Scutelnic, Urs Fischer, Thomas Raphael Meinel, Nedelina Slavova, Jan Gralla, Marwan El-Koussy, Niklaus Denier

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 19% of its field
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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Trial
  2. Vessel segmentation forMagnetic resonance in medicine · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Evaluating migraine with typical aura with neuroimaging.Frontiers in human neuroscience · 2023
    Review
  7. Article
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

10 authors at 1 institution in 1 country.

Philipe Sebastian BreidingInstitute of Diagnostic and Interventional Radiology, Cantonal Hospital Frauenfeld, Spital Thurgau AG, Frauenfeld, Switzerland.ORCID 0000-0002-6985-0332
Frauke Kellner-WeldonInstitute of Diagnostic and Interventional Radiology, Cantonal Hospital Luzern, Luzern, Switzerland.
Lorenz GrunderUniversity Institute of Diagnostic and Interventional Radiology, University of Bern, Inselspital, Bern, Switzerland.
Adrian ScutelnicDepartment of Neurology, University of Bern, Inselspital, Bern, Switzerland.
Urs FischerDepartment of Neurology, University of Bern, Inselspital, Bern, Switzerland.
Thomas Raphael MeinelDepartment of Neurology, University of Bern, Inselspital, Bern, Switzerland.ORCID 0000-0002-0647-9273
Nedelina SlavovaSupport Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern, Switzerland.
Jan GrallaUniversity Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern, Switzerland.
Marwan El-KoussySupport Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern, Switzerland.
Niklaus DenierSupport Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern, Switzerland.
University of Bern · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSusceptibility weighted imaging (SWI) is a very sensitive technique that often depicts prominent focal veins (PFV) in patients with acute migraine with aura (MwA). Interpretation of visual venous asymmetry (VVA) between brain hemispheres on SWI may help support the clinical diagnosis of MwA. Our goal was to develop an automated algorithm for segmentation and quantification of cerebral veins using SWI. MATERIALS AND

methodsExpert readers visually evaluated SWI of patients with acute MwA for VVA. Subsequently a fully automated algorithm based on 3D normalization and 2D imaging processing using SPM and MATLAB image processing software including top-hat transform was used to quantify cerebral veins and to calculate volumetric differences between hemispheres.

resultsFifty patients with MwA were examined with SWI. VVA was present in 20 of 50 patients (40%). In 95% of patients with VVA, the fully automated calculation agreed with the side that visually harboured more PFV. Our algorithm showed a sensitivity of 95%, specificity of 90% and accuracy of 92% for detecting VVA. Patients with VVA had significantly larger vein volume on the hemisphere with more PFV compared to patients without (15.90 ± 5.38 ml vs 11.93 ± 5.31 ml; p = 0.013). The mean difference in venous volume between hemispheres in patients with VVA was larger compared to patients without VVA (16.34 ± 7.76% vs 4.31 ± 3.26% p < 1E-10). The average time between aura onset and SWI correlated negatively with venous volume of the dominant brain hemisphere (r = -0.348; p = 0.038).

conclusionA fully automated algorithm can accurately identify and quantify cerebral venous distribution on SWI. Absolute quantification may be useful for the future assessment of patients with suspected diseases, which may be associated with a unilateral abnormal degree of venous oxygenation.

Indexed as

AlgorithmsImage Processing, Computer-AssistedAdultCerebral VeinsFeasibility StudiesFemaleHumansMagnetic Resonance AngiographyMaleMiddle AgedMigraine with AuraProspective StudiesRetrospective StudiesYoung Adult

Identifiers

PMID32492059
PMCPMC7269254
OpenAlexW3028997526

What OpenQuestion holds

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Registered trials

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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.