Evidence map›Paper›PMID 29157260›Full record

ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2017

High-resolution and accelerated multi-parametric mapping with automated characterization of vessel disease using intravascular MRI.

Guan Wang, Yi Zhang, Shashank Sathyanarayana Hegde, Paul A Bottomley

Open access · goldAbstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2017. 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
1.1field-weighted citation impact, top 20% 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

10 citing papers in PubMed, 11 citations in OpenAlex.

  1. medRxiv : the preprint server for health sciences · 2025
    Article
  2. Frontiers in cardiovascular medicine · 2025
    Article
  3. Article
  4. Article
  5. Non-invasive imaging as the cornerstone of cardiovascular precision medicine.European heart journal. Cardiovascular Imaging · 2022
    Review
  6. Article
  7. Article
  8. Journal of Cardiovascular Magnetic Resonance: 2017/2018 in review.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2019
    Review
  9. Machine learning in cardiovascular magnetic resonance: basic concepts and applications.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2019
    Review
  10. Journal of Cardiovascular Magnetic Resonance 2017.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2018
    Review
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 at 1 institution in 1 country.

Guan WangDepartment of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Yi ZhangDivision of MR Research, Department of Radiology and Radiological Sciences, Johns Hopkins University, Park building 310, 600 N Wolfe Street, Baltimore, MD, 21287, USA.
Shashank Sathyanarayana HegdeDivision of MR Research, Department of Radiology and Radiological Sciences, Johns Hopkins University, Park building 310, 600 N Wolfe Street, Baltimore, MD, 21287, USA.
Paul A BottomleyDepartment of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. bottoml@mri.jhu.edu.
Johns Hopkins University · US

Funding

Advanced MRI Technology in Safety & InterventionR01EB007829 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI BOTTOMLEY, PAUL A · 2008 to 2017
$4.3M
National Institutes of Health R01 EB007829NIBIB NIH HHS R01 EB007829
6 · The paper itself

Abstract

backgroundAtherosclerosis is prevalent in cardiovascular disease, but present imaging modalities have limited capabilities for characterizing lesion stage, progression and response to intervention. This study tests whether intravascular magnetic resonance imaging (IVMRI) measures of relaxation times (T

methodsIVMRI was performed in fresh human artery segments and swine vessels in vivo, using fast multi-parametric sequences, 1-2 mm diameter loopless antennae and 200-300 μm resolution. T

resultsThe SVM accurately classified ~80% of samples into the three disease classes. The 'area-under-the-curve' was 0.96 for detecting disease in 248 samples, with T

conclusionHigh-resolution IVMRI measures of T

Indexed as

AnimalsArea Under CurveAutomationCoronary Artery DiseaseCoronary VesselsHumansIliac ArteryImage Interpretation, Computer-AssistedMagnetic Resonance AngiographyMiddle AgedPeripheral Arterial DiseasePlaque, AtheroscleroticPredictive Value of TestsReproducibility of ResultsROC CurveSeverity of Illness IndexAccelerated acquisitionAtherosclerosisDisease classificationIntravascular MRIMachine learningRelaxation timesSLAM

Identifiers

PMID29157260
PMCPMC5694914
OpenAlexW2769140072

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.