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Reproducibility of Small Animal Cine and Scar Cardiac Magnetic Resonance Imaging Using a Clinical 3.0 Tesla System

Overview
Journal BMC Med Imaging
Publisher Biomed Central
Specialty Radiology
Date 2013 Dec 19
PMID 24345214
Citations 2
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Abstract

Background: To evaluate the inter-study, inter-reader and intra-reader reproducibility of cardiac cine and scar imaging in rats using a clinical 3.0 Tesla magnetic resonance (MR) system.

Methods: Thirty-three adult rats (Sprague-Dawley) were imaged 24 hours after surgical occlusion of the left anterior descending coronary artery using a 3.0 Tesla clinical MR scanner (Philips Healthcare, Best, The Netherlands) equipped with a dedicated 70 mm solenoid receive-only coil. Left-ventricular (LV) volumes, mass, ejection fraction and amount of myocardial scar tissue were measured. Intra-and inter-observer reproducibility was assessed in all animals. In addition, repeat MR exams were performed in 6 randomly chosen rats within 24 hours to assess inter-study reproducibility.

Results: The MR imaging protocol was successfully completed in 32 (97%) animals. Bland-Altman analysis demonstrated high intra-reader reproducibility (mean bias%: LV end-diastolic volume (LVEDV), -1.7%; LV end-systolic volume (LVESV), -2.2%; LV ejection fraction (LVEF), 1.0%; LV mass, -2.7%; and scar mass, -1.2%) and high inter-reader reproducibility (mean bias%: LVEDV, 3.3%; LVESV, 6.2%; LVEF, -4.8%; LV mass, -1.9%; and scar mass, -1.8%). In addition, a high inter-study reproducibility was found (mean bias%: LVEDV, 0.1%; LVESV, -1.8%; LVEF, 1.0%; LV mass, -4.6%; and scar mass, -6.2%).

Conclusions: Cardiac MR imaging of rats yielded highly reproducible measurements of cardiac volumes/function and myocardial infarct size on a clinical 3.0 Tesla MR scanner system. Consequently, more widely available high field clinical MR scanners can be employed for small animal imaging of the heart e.g. when aiming at serial assessments during therapeutic intervention studies.

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Application of Magnetic Resonance Strain Analysis Using Feature Tracking in a Myocardial Infarction Model.

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