Non-contrast enhanced diagnosis of acute myocarditis based on the 17-segment heart model using 2D-feature tracking magnetic resonance imaging.


Journal

Magnetic resonance imaging
ISSN: 1873-5894
Titre abrégé: Magn Reson Imaging
Pays: Netherlands
ID NLM: 8214883

Informations de publication

Date de publication:
01 2020
Historique:
received: 28 08 2019
revised: 01 10 2019
accepted: 05 11 2019
pubmed: 13 11 2019
medline: 30 9 2020
entrez: 13 11 2019
Statut: ppublish

Résumé

The aim of this study was to investigate the diagnostic value of myocardial deformation analysis based on the 17-segment heart model using non-contrast enhanced (CE) 2D tissue feature tracking (2D-FT) technique. Seventy patients with suspected myocarditis underwent a cardiovascular magnetic resonance (CMR) examination at 1.5 Tesla. A contrast-agent-free part of this CMR protocol was additionally performed in forty healthy volunteers (HV). Besides standard CMR data sets, 2D-FT derived segmental and global longitudinal, radial, and circumferential deformation parameters were analyzed. The 2D-FT results were compared to the combined findings from CMR imaging and endomyocardial biopsy (EMB). Patients were assigned to three groups depending on their ejection fraction (EF) (<40%, 40-55%, ≥55%). Compared to HV, impaired EF (<55%) was significantly correlated to reduced segmental and global strain and strain rate values. The circumferential deformation analysis was more sensitive to myocardial changes than longitudinal and radial analysis. The segmental strain/strain rate had an accuracy of 84.3%/70.0% for the diagnosis of an acute myocarditis, stated by EMB and CMR in 42 of 70 patients. In patients with preserved EF, acute myocarditis could be ruled out using only segmental strain analysis with a negative predictive value of 87.5%. In patients with suspected myocarditis, the deformation analysis based on the 17-segment heart model provides valuable information about functional myocardial inhomogeneity. This quantitative approach could be used in addition to the clinical standard CMR protocol and represents a promising tool in the framework of a prospective automatized multiparametric CMR imaging analysis.

Identifiants

pubmed: 31715249
pii: S0730-725X(19)30517-X
doi: 10.1016/j.mri.2019.11.008
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

155-165

Informations de copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

Auteurs

Mona Salehi Ravesh (M)

Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Department for Radiology and Neuroradiology, University Medical Center, Kiel, Germany; Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany. Electronic address: Mona.Salehiravesh@uksh.de.

Matthias Eden (M)

Department for Internal Medicine III, Molecular Cardiology and Angiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany.

Patrick Langguth (P)

Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany.

Tim-Christian Piesch (TC)

Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany.

Johanna Karolin Lehmann (JK)

Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany.

Annett Lebenatus (A)

Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany.

David Hauttemann (D)

Medis medical imaging systems bv, Leiden, Netherlands.

Joachim Graessner (J)

Siemens Healthcare GmbH, Hamburg, Germany.

Norbert Frey (N)

Department for Internal Medicine III, Molecular Cardiology and Angiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany.

Olav Jansen (O)

Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany.

Marcus Both (M)

Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany.

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