Evidence map›Paper›PMID 35344593›Full record

ArticleMagnetic resonance in medicine2022

Simultaneous multislice steady-state free precession myocardial perfusion with full left ventricular coverage and high resolution at 1.5 T.

Sarah McElroy, Giulio Ferrazzi, Muhummad Sohaib Nazir, Carl Evans, Joana Ferreira, Filippo Bosio, Nabila Mughal, Karl P Kunze, Radhouene Neji, Peter Speier and 7 more

Open access · hybridAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.3field-weighted citation impact, top 22% 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

8 citing papers in PubMed, 10 citations in OpenAlex.

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  8. Customizing native T1 mapping: The effects of compressed sensing, deep learning-based denoising, and high-resolution on measurement of native myocardial T1.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
    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

17 authors at 5 institutions in 5 countries.

Sarah McElroySchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.ORCID 0000-0002-4271-5931
Giulio FerrazziIRCCS San Camillo Hospital, Venice, Italy.ORCID 0000-0001-7449-4718
Muhummad Sohaib NazirSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.ORCID 0000-0003-2749-1033
Carl EvansSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Joana FerreiraSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Filippo BosioSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Nabila MughalSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Karl P KunzeMR Research Collaborations, Siemens Healthcare Limited, Frimley, England, UK.
Radhouene NejiSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Peter SpeierMagnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany.ORCID 0000-0003-0449-1145
Daniel StäbMR Research Collaborations, Siemens Healthcare Limited, Melbourne, Australia.ORCID 0000-0002-5738-7803
Tevfik F IsmailSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Pier Giorgio MasciSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Adriana D M VillaSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Reza RazaviSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Amedeo ChiribiriSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.ORCID 0000-0003-3394-4289
Sébastien RoujolSchool of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.ORCID 0000-0002-7146-0552
King's College London · GBIRCCS San Camillo Hospital · ITSiemens Healthcare (United States) · USSiemens Healthineers (Germany) · DESiemens (United Kingdom) · GB

Funding

British Heart Foundation PG/19/11/34243Wellcome TrustWellcome Trust WT 203148/Z/16/Z
6 · The paper itself

Abstract

purposeTo implement and evaluate a simultaneous multi-slice balanced SSFP (SMS-bSSFP) perfusion sequence and compressed sensing reconstruction for cardiac MR perfusion imaging with full left ventricular (LV) coverage (nine slices/heartbeat) and high spatial resolution (1.4 × 1.4 mm

methodsA preliminary study was performed to evaluate the performance of blipped controlled aliasing in parallel imaging (CAIPI) and RF-CAIPI with gradient-controlled local Larmor adjustment (GC-LOLA) in the presence of fat. A nine-slice SMS-bSSFP sequence using RF-CAIPI with GC-LOLA with high spatial resolution (1.4 × 1.4 mm

resultsFat signal leakage was significantly higher for blipped CAIPI than for RF-CAIPI with GC-LOLA (7.9% vs. 1.2%, p = 0.010). All 10 SMS-bSSFP perfusion datasets resulted in 16/16 diagnostic myocardial segments. There were no significant differences between the SMS and conventional acquisitions in terms of image quality (2.6 ± 0.6 vs. 2.7 ± 0.2, p = 0.8) or perceived SNR (2.8 ± 0.3 vs. 2.7 ± 0.3, p = 0.3). Inter-reader variability was good for both image quality (ICC = 0.84) and perceived SNR (ICC = 0.70). Myocardial sharpness was improved using the SMS sequence compared to the conventional sequence (0.37 ± 0.08 vs 0.32 ± 0.05, p < 0.001). There was no significant difference between measurements of upslope index for the SMS and conventional sequences (0.11 ± 0.04 vs. 0.11 ± 0.03, p = 0.84).

conclusionSMS-bSSFP with multiband factor 3 and compressed sensing reconstruction enables cardiac MR perfusion imaging with three-fold increased spatial coverage and improved myocardial sharpness compared to a conventional sequence, without compromising perceived SNR, image quality, upslope index or number of diagnostic segments.

Indexed as

Image EnhancementImage Interpretation, Computer-AssistedHeart VentriclesHumansPerfusionReproducibility of Resultscompressed sensingmyocardial perfusionsimultaneous multi-slice

Identifiers

PMID35344593
PMCPMC9310832
OpenAlexW4220663103

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.