Evidence map›Paper›PMID 30208894›Full record

ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2018

Comparison of left ventricular strains and torsion derived from feature tracking and DENSE CMR.

Gregory J Wehner, Linyuan Jing, Christopher M Haggerty, Jonathan D Suever, Jing Chen, Sean M Hamlet, Jared A Feindt, W Dimitri Mojsejenko, Mark A Fogel, Brandon K Fornwalt

Abstract readComparative StudyEvaluation StudyMulticenter Study
In one paragraph

Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
–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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
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  5. Article
  6. Article
  7. Article
  8. Automated segmentation of long and short axis DENSE cardiovascular magnetic resonance for myocardial strain analysis using spatio-temporal convolutional neural networks.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023
    Article
  9. Article
  10. Right-left ventricular shape variations in tetralogy of Fallot: associations with pulmonary regurgitation.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2021
    Article
  11. Article
  12. Impact of pulmonary valve replacement on left ventricular rotational mechanics in repaired tetralogy of Fallot.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2021
    Article
  13. Comparing cardiovascular magnetic resonance strain software packages by their abilities to discriminate outcomes in patients with heart failure with preserved ejection fraction.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2021
    Article
  14. Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2021
    Article
  15. Article
  16. Article
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  18. 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
  19. Echocardiography and cardiovascular magnetic resonance based evaluation of myocardial strain and relationship with late gadolinium enhancement.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2019
    Article
  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

10 authors.

Gregory J WehnerDepartment of Biomedical Engineering, University of Kentucky, Lexington, KY, USA.
Linyuan JingDepartment of Imaging Science and Innovation, Geisinger, 100 North Academy Avenue, Danville, PA, 17822-4400, USA.
Christopher M HaggertyDepartment of Imaging Science and Innovation, Geisinger, 100 North Academy Avenue, Danville, PA, 17822-4400, USA.
Jonathan D SueverDepartment of Imaging Science and Innovation, Geisinger, 100 North Academy Avenue, Danville, PA, 17822-4400, USA.
Jing ChenDepartment of Imaging Science and Innovation, Geisinger, 100 North Academy Avenue, Danville, PA, 17822-4400, USA.
Sean M HamletDepartment of Electrical Engineering, University of Kentucky, Lexington, KY, USA.
Jared A FeindtDepartment of Imaging Science and Innovation, Geisinger, 100 North Academy Avenue, Danville, PA, 17822-4400, USA.
W Dimitri MojsejenkoDepartment of Pediatrics, University of Kentucky, Lexington, KY, USA.
Mark A FogelDivision of Cardiology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Brandon K FornwaltDepartment of Biomedical Engineering, University of Kentucky, Lexington, KY, USA. bkf@gatech.edu.ORCID 0000-0002-6231-9442

Funding

The role of IKKß in linking obesity to adipose tissue inflammation and adipogeneP20GM103527 · NIGMS · UNIVERSITY OF KENTUCKY · PI REN, HONGMEI · 2012 to 2017
$13.7M
Kentucky Center for Clinical and Translational ScienceUL1TR000117 · NCATS · UNIVERSITY OF KENTUCKY · PI KERN, PHILIP A · 2012 to 2015
$13.3M
Pilot Projects ProgramP30GM127211 · NIGMS · UNIVERSITY OF KENTUCKY · PI CASSIS, LISA A · 2018 to 2022
$5.7M
Exploring the Role of Dyssynchrony in Pediatric Heart Disease with MRIDP5OD012132 · OD · UNIVERSITY OF KENTUCKY · PI FORNWALT, BRANDON K · 2012 to 2016
$2.2M
Interdisciplinary Cardiovascular Training ProgramT32HL072743 · NHLBI · UNIVERSITY OF KENTUCKY · PI RANDALL, DAVID C · 2004 to 2013
$1.3M
NCATS NIH HHS UL1 TR000117NHLBI NIH HHS T32 HL072743NIGMS NIH HHS P20 GM103527NIGMS NIH HHS P30 GM127211NIH HHS DP5 OD012132
6 · The paper itself

Abstract

backgroundCardiovascular magnetic resonance (CMR) feature tracking is increasingly used to quantify cardiac mechanics from cine CMR imaging, although validation against reference standard techniques has been limited. Furthermore, studies have suggested that commonly-derived metrics, such as peak global strain (reported in 63% of feature tracking studies), can be quantified using contours from just two frames - end-diastole (ED) and end-systole (ES) - without requiring tracking software. We hypothesized that mechanics derived from feature tracking would not agree with those derived from a reference standard (displacement-encoding with stimulated echoes (DENSE) imaging), and that peak strain from feature tracking would agree with that derived using simple processing of only ED and ES contours.

methodsWe retrospectively identified 88 participants with 186 pairs of DENSE and balanced steady state free precession (bSSFP) image slices acquired at the same locations across two institutions. Left ventricular (LV) strains, torsion, and dyssynchrony were quantified from both feature tracking (TomTec Imaging Systems, Circle Cardiovascular Imaging) and DENSE. Contour-based strains from bSSFP images were derived from ED and ES contours. Agreement was assessed with Bland-Altman analyses and coefficients of variation (CoV). All biases are reported in absolute percentage.

resultsComparison results were similar for both vendor packages (TomTec and Circle), and thus only TomTec Imaging System data are reported in the abstract for simplicity. Compared to DENSE, mid-ventricular circumferential strain (Ecc) from feature tracking had acceptable agreement (bias: - 0.4%, p = 0.36, CoV: 11%). However, feature tracking significantly overestimated the magnitude of Ecc at the base (bias: - 4.0% absolute, p < 0.001, CoV: 18%) and apex (bias: - 2.4% absolute, p = 0.01, CoV: 15%), underestimated torsion (bias: - 1.4 deg/cm, p < 0.001, CoV: 41%), and overestimated dyssynchrony (bias: 26 ms, p < 0.001, CoV: 76%). Longitudinal strain (Ell) had borderline-acceptable agreement (bias: - 0.2%, p = 0.77, CoV: 19%). Contour-based strains had excellent agreement with feature tracking (biases: - 1.3-0.2%, CoVs: 3-7%).

conclusionCompared to DENSE as a reference standard, feature tracking was inaccurate for quantification of apical and basal LV circumferential strains, longitudinal strain, torsion, and dyssynchrony. Feature tracking was only accurate for quantification of mid LV circumferential strain. Moreover, feature tracking is unnecessary for quantification of whole-slice strains (e.g. base, apex), since simplified processing of only ED and ES contours yields very similar results to those derived from feature tracking. Current feature tracking technology therefore has limited utility for quantification of cardiac mechanics.

Indexed as

Myocardial ContractionVentricular Function, LeftAdolescentAdultBiomechanical PhenomenaChildDatabases, FactualFemaleHumansImage Interpretation, Computer-AssistedKentuckyMagnetic Resonance Imaging, CineMalePhiladelphiaPredictive Value of TestsReference StandardsDENSEDyssynchronyFeature trackingStrainTorsion

Identifiers

PMID30208894
PMCPMC6136226

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.