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Quantitative myocardial perfusion in coronary artery disease: A perfusion mapping study

Knott, KD; Camaioni, C; Ramasamy, A; Augusto, JA; Bhuva, AN; Xue, H; Manisty, C; ... Moon, JC; + view all (2019) Quantitative myocardial perfusion in coronary artery disease: A perfusion mapping study. Journal of Magnetic Resonance Imaging 10.1002/jmri.26668. (In press). Green open access

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Abstract

BACKGROUND: Cardiac MR stress perfusion remains a qualitative technique in clinical practice due to technical and postprocessing challenges. However, automated inline perfusion mapping now permits myocardial blood flow (MBF, ml/g/min) quantification on-the-fly without user input. PURPOSE: To investigate the diagnostic performance of this novel technique in detecting occlusive coronary artery disease (CAD) in patients scheduled to undergo coronary angiography. STUDY TYPE: Prospective, observational. SUBJECTS: Fifty patients with suspected CAD and 24 healthy volunteers. FIELD STRENGTH: 1.5T. SEQUENCE: "Dual" sequence multislice 2D saturation recovery. ASSESSMENT: All patients underwent cardiac MR with perfusion mapping and invasive coronary angiography; the healthy volunteers had MR with perfusion mapping alone. STATISTICAL TESTS: Comparison between numerical variables was performed using an independent t-test. Receiver operator characteristic (ROC) curves were generated for transmyocardial, endocardial stress MBF, and myocardial perfusion reserve (MPR, the stress:rest MBF ratio) to diagnose severe (>70%) stenoses as measured by 3D quantitative coronary angiography (QCA). ROC curves were compared by the method of DeLong et al. RESULTS: Compared with volunteers, patients had lower stress MBF and MPR even in vessels with <50% stenosis (2.00 vs. 3.08 ml/g/min, respectively). As stenosis severity increased (<50%, 50-70%, >70%), MBF and MPR decreased. To diagnose occlusive (>70%) CAD, endocardial and transmyocardial stress MBF were superior to MPR (area under the curve 0.92 [95% CI 0.86-0.97] vs. 0.90 [95% CI 0.84-0.95] and 0.80 [95% CI 0.72-0.87], respectively). An endocardial threshold of 1.31 ml/g/min provided a per-coronary artery sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 90%, 82%, 50%, and 98%, with a per-patient diagnostic performance of 100%, 66%, 57%, and 100%, respectively. DATA CONCLUSION: Perfusion mapping can diagnose occlusive CAD with high accuracy and, in particular, high sensitivity and NPV make it a potential "rule-out" test. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2.

Type: Article
Title: Quantitative myocardial perfusion in coronary artery disease: A perfusion mapping study
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/jmri.26668
Publisher version: http://doi.org/10.1002/jmri.26668
Language: English
Additional information: Copyright © 2019 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: cardiovascular magnetic resonance, coronary artery disease, inline perfusion quantification, myocardial perfusion, perfusion mapping
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Clinical Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10067109
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