Evidence map›Paper›PMID 24030840›Full record

ArticleMagnetic resonance in medicine2014

Towards elimination of the dark-rim artifact in first-pass myocardial perfusion MRI: removing Gibbs ringing effects using optimized radial imaging.

Behzad Sharif, Rohan Dharmakumar, Troy LaBounty, Reza Arsanjani, Chrisandra Shufelt, Louise Thomson, C Noel Bairey Merz, Daniel S Berman, Debiao Li

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Article
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  4. Retrospective Detection and Suppression of Dark-Rim Artifacts in First-Pass Perfusion Cardiac MRI Enabled by Deep Learning.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021
    Article
  5. Article
  6. Article
  7. Article
  8. [Elimination of Gibbs artifact based on local subpixel shift and interlaced local variation].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2019
    Article
  9. Article
  10. Article
  11. Women's health: making cardiovascular disease real.Current opinion in cardiology · 2018
    Review
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  14. Article
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  16. Review
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  18. Article
  19. Coronary Microvascular Dysfunction - Epidemiology, Pathogenesis, Prognosis, Diagnosis, Risk Factors and Therapy.Circulation journal : official journal of the Japanese Circulation Society · 2016
    Review
  20. 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

9 authors.

Behzad SharifBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Rohan Dharmakumar
Troy LaBounty
Reza Arsanjani
Chrisandra Shufelt
Louise Thomson
C Noel Bairey Merz
Daniel S Berman
Debiao Li

Funding

Women's Ischemia Syndrome Evaluation (WISE) Coronary Vascular DysfunctionR01HL090957 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI BAIREY MERZ, CATHLEEN NOEL, PEPINE, CARL J · 2008 to 2013
$3.5M
Reliable Evaluation of Coronary Artery Disease using Myocardial BOLD MRI with CO2R01HL091989 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI DHARMAKUMAR, ROHAN · 2008 to 2016
$3.5M
Magnetic Resonance Imaging of Coronary Arteries at 3.OTR01EB002623 · NIBIB · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LI, DEBIAO · 2003 to 2012
$2.6M
Self-Gated, Motion-Resolved Whole-Heart Coronary MRA with Isotropic ResolutionR01HL038698 · NHLBI · WASHINGTON UNIVERSITY · PI LI, DEBIAO · 1990 to 2010
$2.5M
NHLBI NIH HHS R01 HL038698NHLBI NIH HHS R01 HL090957NHLBI NIH HHS R01-HL090957NHLBI NIH HHS R01 HL091989NHLBI NIH HHS R01-HL091989NHLBI NIH HHS R01-HL38698NHLBI NIH HHS U01-HL649241NIBIB NIH HHS R01 EB002623
6 · The paper itself

Abstract

purposeSubendocardial dark-rim artifacts (DRAs) remain a major concern in first-pass perfusion (FPP) myocardial MRI and may lower the diagnostic accuracy for detection of ischemia. A major source of DRAs is the "Gibbs ringing" effect. We propose an optimized radial acquisition strategy aimed at eliminating ringing-induced DRAs in FPP. THEORY AND

methodsBy studying the underlying point spread function (PSF), we show that optimized radial sampling with a simple reconstruction method can eliminate the oscillations in the PSF that cause ringing artifacts. We conducted realistic MRI phantom experiments and in vivo studies (n = 12 healthy humans) to evaluate the artifact behavior of the proposed imaging scheme in comparison to a conventional Cartesian imaging protocol.

resultsSimulations and phantom experiments verified our theoretical expectations. The in vivo studies showed that optimized radial imaging is capable of significantly reducing DRAs in the early myocardial enhancement phase (during which the ringing effect is most prominent and may obscure perfusion defects) while providing similar resolution and image quality compared with conventional Cartesian imaging.

conclusionThe developed technical framework and results demonstrate that, in comparison to conventional Cartesian techniques, optimized radial imaging with the proposed optimizations significantly reduces the prevalence and spatial extent of DRAs in FPP imaging.

Indexed as

MyocardiumArtifactsComputer SimulationContrast MediaFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMyocardial IschemiaOrganometallic CompoundsPhantoms, ImagingYoung AdultContrast MediagadoversetamideOrganometallic Compoundsdark-rim artifactfirst-pass perfusion MRIGibbs ringingmyocardial perfusionradial samplingsubendocardial ischemia

Identifiers

PMID24030840
PMCPMC4176898

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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.