Evidence map›Paper›PMID 34485397›Full record

ReviewFrontiers in cardiovascular medicine2021

Coronary Magnetic Resonance Angiography in Chronic Coronary Syndromes.

Reza Hajhosseiny, Camila Munoz, Gastao Cruz, Ramzi Khamis, Won Yong Kim, Claudia Prieto, René M Botnar

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
3.1field-weighted citation impact, top 8% 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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.

  1. Diagnostic accuracy of whole heart coronary magnetic resonance angiography: a systematic review and meta-analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023
    Pooled it
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  7. Automatic vessel segmentation and reformation of non-contrast coronary magnetic resonance angiography using transfer learning-based three-dimensional U-net with attention mechanism.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025
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  12. Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023
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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

7 authors at 4 institutions in 3 countries.

Reza HajhosseinySchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Camila MunozSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Gastao CruzSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Ramzi KhamisNational Heart and Lung Institute, Imperial College London, London, United Kingdom.
Won Yong KimDepartment of Cardiology and Institute of Clinical Medicine, Aarhus University Hospital, Skejby, Denmark.
Claudia PrietoSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
René M BotnarSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
King's College London · GBAarhus University Hospital · DKImperial College London · GBPontificia Universidad Católica de Chile · CL

Funding

British Heart Foundation FS/17/16/32560British Heart Foundation FS/CRTF/20/24011Medical Research Council MR/L009676/1
6 · The paper itself

Abstract

Cardiovascular disease is the leading cause of mortality worldwide, with atherosclerotic coronary artery disease (CAD) accounting for the majority of cases. X-ray coronary angiography and computed tomography coronary angiography (CCTA) are the imaging modalities of choice for the assessment of CAD. However, the use of ionising radiation and iodinated contrast agents remain drawbacks. There is therefore a clinical need for an alternative modality for the early identification and longitudinal monitoring of CAD without these associated drawbacks. Coronary magnetic resonance angiography (CMRA) could be a potential alternative for the detection and monitoring of coronary arterial stenosis, without exposing patients to ionising radiation or iodinated contrast agents. Further advantages include its versatility, excellent soft tissue characterisation and suitability for repeat imaging. Despite the early promise of CMRA, widespread clinical utilisation remains limited due to long and unpredictable scan times, onerous scan planning, lower spatial resolution, as well as motion related image quality degradation. The past decade has brought about a resurgence in CMRA technology, with significant leaps in image acceleration, respiratory and cardiac motion estimation and advanced motion corrected or motion-resolved image reconstruction. With the advent of artificial intelligence, great advances are also seen in deep learning-based motion estimation, undersampled and super-resolution reconstruction promising further improvements of CMRA. This has enabled high spatial resolution (1 mm isotropic), 3D whole heart CMRA in a clinically feasible and reliable acquisition time of under 10 min. Furthermore, latest super-resolution image reconstruction approaches which are currently under evaluation promise acquisitions as short as 1 min. In this review, we will explore the recent technological advances that are designed to bring CMRA closer to clinical reality.

Indexed as

atherosclerosisCCSCMRAcoronary angiographymagnetic resonance imagingplaque

Identifiers

PMID34485397
PMCPMC8416045
OpenAlexW3194109163

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.