Tushar Kotecha1, Liza Chacko2, Omar Chehab3, Nanci O'Reilly3, Ana Martinez-Naharro2, Jonathan Lazari4, Kristopher D Knott5, James Brown1, Daniel Knight1, Vivek Muthurangu6, Philip Hawkins2, Sven Plein7, James C Moon5, Hui Xue8, Peter Kellman8, Roby Rakhit1, Niket Patel1, Marianna Fontana9. 1. Institute of Cardiovascular Science, University College London, United Kingdom; Department of Cardiology, Royal Free Hospital, London, United Kingdom. 2. Department of Cardiology, Royal Free Hospital, London, United Kingdom; Division of Medicine, University College London, London, United Kingdom. 3. Department of Cardiology, Barts Heart Centre, London, United Kingdom. 4. Department of Cardiology, Royal Free Hospital, London, United Kingdom. 5. Institute of Cardiovascular Science, University College London, United Kingdom; Department of Cardiology, Barts Heart Centre, London, United Kingdom. 6. Institute of Cardiovascular Science, University College London, United Kingdom. 7. Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom. 8. Medical Signal and Image Processing Program, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA. 9. Department of Cardiology, Royal Free Hospital, London, United Kingdom; Division of Medicine, University College London, London, United Kingdom. Electronic address: m.fontana@ucl.ac.uk.
Abstract
OBJECTIVES: The authors sought to compare the diagnostic accuracy of quantitative perfusion maps to visual assessment (VA) of first-pass perfusion images for the detection of multivessel coronary artery disease (MVCAD). BACKGROUND: VA of first-pass stress perfusion cardiac magnetic resonance (CMR) may underestimate ischemia in MVCAD. Pixelwise perfusion mapping allows quantitative measurement of regional myocardial blood flow, which may improve ischemia detection in MVCAD. METHODS: One hundred fifty-one subjects recruited at 2 centers underwent stress perfusion CMR with myocardial perfusion mapping, and invasive coronary angiography with coronary physiology assessment. Ischemic burden was assessed by VA of first-pass images and by quantitative measurement of stress myocardial blood flow using perfusion maps. RESULTS: In patients with MVCAD (2-vessel [2VD] or 3-vessel disease [3VD]; n = 95), perfusion mapping identified significantly more segments with perfusion defects (median segments per patient 12 [interquartile range (IQR): 9 to 16] by mapping vs. 8 [IQR: 5 to 9.5] by VA; p < 0.001). Ischemic burden (IB) measured using mapping was higher in MVCAD compared with IB measured using VA (3VD mapping 100 % (75% to 100%) vs. first-pass 56% (38% to 81%) ; 2VD mapping 63% (50% to 75%) vs. first-pass 41% (31% to 50%); both p < 0.001), but there was no difference in single-vessel disease (mapping 25% (13% to 44%) vs. 25% (13% to 31%). Perfusion mapping was superior to VA for the correct identification of extent of coronary disease (78% vs. 58%; p < 0.001) due to better identification of 3VD (87% vs. 40%) and 2VD (71% vs. 48%). CONCLUSIONS: VA of first-pass stress perfusion underestimates ischemic burden in MVCAD. Pixelwise quantitative perfusion mapping increases the accuracy of CMR in correctly identifying extent of coronary disease. This has important implications for assessment of ischemia and therapeutic decision-making.
OBJECTIVES: The authors sought to compare the diagnostic accuracy of quantitative perfusion maps to visual assessment (VA) of first-pass perfusion images for the detection of multivessel coronary artery disease (MVCAD). BACKGROUND:VA of first-pass stress perfusion cardiac magnetic resonance (CMR) may underestimate ischemia in MVCAD. Pixelwise perfusion mapping allows quantitative measurement of regional myocardial blood flow, which may improve ischemia detection in MVCAD. METHODS: One hundred fifty-one subjects recruited at 2 centers underwent stress perfusion CMR with myocardial perfusion mapping, and invasive coronary angiography with coronary physiology assessment. Ischemic burden was assessed by VA of first-pass images and by quantitative measurement of stress myocardial blood flow using perfusion maps. RESULTS: In patients with MVCAD (2-vessel [2VD] or 3-vessel disease [3VD]; n = 95), perfusion mapping identified significantly more segments with perfusion defects (median segments per patient 12 [interquartile range (IQR): 9 to 16] by mapping vs. 8 [IQR: 5 to 9.5] by VA; p < 0.001). Ischemic burden (IB) measured using mapping was higher in MVCAD compared with IB measured using VA (3VD mapping 100 % (75% to 100%) vs. first-pass 56% (38% to 81%) ; 2VD mapping 63% (50% to 75%) vs. first-pass 41% (31% to 50%); both p < 0.001), but there was no difference in single-vessel disease (mapping 25% (13% to 44%) vs. 25% (13% to 31%). Perfusion mapping was superior to VA for the correct identification of extent of coronary disease (78% vs. 58%; p < 0.001) due to better identification of 3VD (87% vs. 40%) and 2VD (71% vs. 48%). CONCLUSIONS:VA of first-pass stress perfusion underestimates ischemic burden in MVCAD. Pixelwise quantitative perfusion mapping increases the accuracy of CMR in correctly identifying extent of coronary disease. This has important implications for assessment of ischemia and therapeutic decision-making.
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