Tushar Kotecha1, Ana Martinez-Naharro2, Michele Boldrini3, Daniel Knight1, Philip Hawkins2, Sundeep Kalra4, Deven Patel4, Gerry Coghlan4, James Moon5, Sven Plein6, Tim Lockie4, Roby Rakhit1, Niket Patel1, Hui Xue7, Peter Kellman7, Marianna Fontana8. 1. Institute of Cardiovascular Science, University College London, United Kingdom; Royal Free Hospital, London, United Kingdom. 2. Royal Free Hospital, London, United Kingdom; Division of Medicine, University College London, United Kingdom. 3. Division of Medicine, University College London, United Kingdom. 4. Royal Free Hospital, London, United Kingdom. 5. Institute of Cardiovascular Science, University College London, United Kingdom; Barts Heart Centre, London, United Kingdom. 6. Institute of Cardiovascular and Metabolic Medicine, University of Leeds, United Kingdom. 7. National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, Maryland. 8. Royal Free Hospital, London, United Kingdom; Division of Medicine, University College London, United Kingdom. Electronic address: m.fontana@ucl.ac.uk.
Abstract
OBJECTIVES: This study sought to assess the performance of cardiovascular magnetic resonance (CMR) myocardial perfusion mapping against invasive coronary physiology reference standards for detecting coronary artery disease (CAD, defined by fractional flow reserve [FFR] ≤0.80), microvascular dysfunction (MVD) (defined by index of microcirculatory resistance [IMR] ≥25) and the ability to differentiate between the two. BACKGROUND: Differentiation of epicardial (CAD) and MVD in patients with stable angina remains challenging. Automated in-line CMR perfusion mapping enables quantification of myocardial blood flow (MBF) to be performed rapidly within a clinical workflow. METHODS: Fifty patients with stable angina and 15 healthy volunteers underwent adenosine stress CMR at 1.5T with quantification of MBF and myocardial perfusion reserve (MPR). FFR and IMR were measured in 101 coronary arteries during subsequent angiography. RESULTS: Twenty-seven patients had obstructive CAD and 23 had nonobstructed arteries (7 normal IMR, 16 abnormal IMR). FFR positive (epicardial stenosis) areas had significantly lower stress MBF (1.47 ± 0.48 ml/g/min) and MPR (1.75 ± 0.60) than FFR-negative IMR-positive (MVD) areas (stress MBF: 2.10 ± 0.35 ml/g/min; MPR: 2.41 ± 0.79) and normal areas (stress MBF: 2.47 ± 0.50 ml/g/min; MPR: 2.94 ± 0.81). Stress MBF ≤1.94 ml/g/min accurately detected obstructive CAD on a regional basis (area under the curve: 0.90; p < 0.001). In patients without regional perfusion defects, global stress MBF <1.82 ml/g/min accurately discriminated between obstructive 3-vessel disease and MVD (area under the curve: 0.94; p < 0.001). CONCLUSIONS: This novel automated pixel-wise perfusion mapping technique can be used to detect physiologically significant CAD defined by FFR, MVD defined by IMR, and to differentiate MVD from multivessel coronary disease. A CMR-based diagnostic algorithm using perfusion mapping for detection of epicardial disease and MVD warrants further clinical validation.
OBJECTIVES: This study sought to assess the performance of cardiovascular magnetic resonance (CMR) myocardial perfusion mapping against invasive coronary physiology reference standards for detecting coronary artery disease (CAD, defined by fractional flow reserve [FFR] ≤0.80), microvascular dysfunction (MVD) (defined by index of microcirculatory resistance [IMR] ≥25) and the ability to differentiate between the two. BACKGROUND: Differentiation of epicardial (CAD) and MVD in patients with stable angina remains challenging. Automated in-line CMR perfusion mapping enables quantification of myocardial blood flow (MBF) to be performed rapidly within a clinical workflow. METHODS: Fifty patients with stable angina and 15 healthy volunteers underwent adenosine stress CMR at 1.5T with quantification of MBF and myocardial perfusion reserve (MPR). FFR and IMR were measured in 101 coronary arteries during subsequent angiography. RESULTS: Twenty-seven patients had obstructive CAD and 23 had nonobstructed arteries (7 normal IMR, 16 abnormal IMR). FFR positive (epicardial stenosis) areas had significantly lower stress MBF (1.47 ± 0.48 ml/g/min) and MPR (1.75 ± 0.60) than FFR-negative IMR-positive (MVD) areas (stress MBF: 2.10 ± 0.35 ml/g/min; MPR: 2.41 ± 0.79) and normal areas (stress MBF: 2.47 ± 0.50 ml/g/min; MPR: 2.94 ± 0.81). Stress MBF ≤1.94 ml/g/min accurately detected obstructive CAD on a regional basis (area under the curve: 0.90; p < 0.001). In patients without regional perfusion defects, global stress MBF <1.82 ml/g/min accurately discriminated between obstructive 3-vessel disease and MVD (area under the curve: 0.94; p < 0.001). CONCLUSIONS: This novel automated pixel-wise perfusion mapping technique can be used to detect physiologically significant CAD defined by FFR, MVD defined by IMR, and to differentiate MVD from multivessel coronary disease. A CMR-based diagnostic algorithm using perfusion mapping for detection of epicardial disease and MVD warrants further clinical validation.
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