Avinash Kali1, Eui-Young Choi2, Behzad Sharif3, Young Jin Kim4, Xiaoming Bi5, Bruce Spottiswoode6, Ivan Cokic3, Hsin-Jung Yang1, Mourad Tighiouart7, Antonio Hernandez Conte8, Debiao Li9, Daniel S Berman10, Byoung Wook Choi4, Hyuk-Jae Chang11, Rohan Dharmakumar12. 1. Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; Department of Bioengineering, University of California, Los Angeles, California. 2. Division of Cardiology, Yonsei University College of Medicine, Seoul, South Korea. 3. Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California. 4. Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea. 5. MR Research & Development, Siemens Healthcare, Los Angeles, California. 6. MR Research & Development, Siemens Healthcare, Chicago, Illinois. 7. Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center, Los Angeles, California. 8. Department of Anesthesiology, Cedars-Sinai Medical Center, Los Angeles, California. 9. Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; Department of Bioengineering, University of California, Los Angeles, California; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California. 10. Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California. 11. Division of Cardiology, Yonsei University College of Medicine, Seoul, South Korea. Electronic address: hjchang@yuhs.ac. 12. Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; Department of Bioengineering, University of California, Los Angeles, California; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California. Electronic address: rohandkumar@csmc.edu.
Abstract
OBJECTIVES: The purpose of this study was to investigate whether native T1 maps at 3-T can reliably characterize chronic myocardial infarctions (MIs) in patients with prior ST-segment elevation myocardial infarction (STEMI) or non-ST-segment elevation myocardial infarction (NSTEMI). BACKGROUND: Late gadolinium enhancement (LGE) cardiac magnetic resonance is the gold standard for characterizing chronic MIs, but it is contraindicated in patients with end-stage chronic kidney disease. METHODS: Native T1 and LGE images were acquired at 3-T in patients with prior STEMI (n = 13) and NSTEMI (n = 12) at a median of 13.6 years post-MI. Infarct location, size, and transmurality were measured using mean ± 5 SDs thresholding criterion from LGE images and T1 maps and compared against one another. Independent reviewers assessed visual conspicuity of MIs on LGE images and T1 maps. RESULTS: Native T1 maps and LGE images were not different for measuring infarct size (STEMI: p = 0.46; NSTEMI: p = 0.27) and transmurality (STEMI: p = 0.13; NSTEMI: p = 0.21) using thresholding criterion. Using thresholding criterion, good agreement was observed between LGE images and T1 maps for measuring infarct size (STEMI: bias = 0.6 ± 3.1%; R(2) = 0.93; NSTEMI: bias = -0.4 ± 4.4%; R(2) = 0.85) and transmurality (STEMI: bias = 2.0 ± 4.2%; R(2) = 0.89; NSTEMI: bias = -2.7 ± 7.9%; R(2) = 0.68). Sensitivity and specificity of T1 maps for detecting chronic MIs based on thresholding criterion were 89% and 98%, respectively (STEMI), and 87% and 95%, respectively (NSTEMI). Relative to LGE images, the mean visual conspicuity score for detecting chronic MIs was significantly lower for T1 maps (p < 0.001 for both cases). Median infarct-to-remote myocardium contrast-to-noise ratio was 2.5-fold higher for LGE images relative to T1 maps (p < 0.001). Sensitivity and specificity of T1 maps for visual detection were 60% and 86%, respectively (STEMI), and 64% and 91% (NSTEMI), respectively. CONCLUSIONS: Chronic MIs in STEMI and NSTEMI patients can be reliably characterized using threshold-based detection on native T1 maps at 3-T. Visual detection of chronic MIs on native T1 maps in both patient populations has high specificity, but modest sensitivity.
OBJECTIVES: The purpose of this study was to investigate whether native T1 maps at 3-T can reliably characterize chronic myocardial infarctions (MIs) in patients with prior ST-segment elevation myocardial infarction (STEMI) or non-ST-segment elevation myocardial infarction (NSTEMI). BACKGROUND: Late gadolinium enhancement (LGE) cardiac magnetic resonance is the gold standard for characterizing chronic MIs, but it is contraindicated in patients with end-stage chronic kidney disease. METHODS: Native T1 and LGE images were acquired at 3-T in patients with prior STEMI (n = 13) and NSTEMI (n = 12) at a median of 13.6 years post-MI. Infarct location, size, and transmurality were measured using mean ± 5 SDs thresholding criterion from LGE images and T1 maps and compared against one another. Independent reviewers assessed visual conspicuity of MIs on LGE images and T1 maps. RESULTS: Native T1 maps and LGE images were not different for measuring infarct size (STEMI: p = 0.46; NSTEMI: p = 0.27) and transmurality (STEMI: p = 0.13; NSTEMI: p = 0.21) using thresholding criterion. Using thresholding criterion, good agreement was observed between LGE images and T1 maps for measuring infarct size (STEMI: bias = 0.6 ± 3.1%; R(2) = 0.93; NSTEMI: bias = -0.4 ± 4.4%; R(2) = 0.85) and transmurality (STEMI: bias = 2.0 ± 4.2%; R(2) = 0.89; NSTEMI: bias = -2.7 ± 7.9%; R(2) = 0.68). Sensitivity and specificity of T1 maps for detecting chronic MIs based on thresholding criterion were 89% and 98%, respectively (STEMI), and 87% and 95%, respectively (NSTEMI). Relative to LGE images, the mean visual conspicuity score for detecting chronic MIs was significantly lower for T1 maps (p < 0.001 for both cases). Median infarct-to-remote myocardium contrast-to-noise ratio was 2.5-fold higher for LGE images relative to T1 maps (p < 0.001). Sensitivity and specificity of T1 maps for visual detection were 60% and 86%, respectively (STEMI), and 64% and 91% (NSTEMI), respectively. CONCLUSIONS: Chronic MIs in STEMI and NSTEMI patients can be reliably characterized using threshold-based detection on native T1 maps at 3-T. Visual detection of chronic MIs on native T1 maps in both patient populations has high specificity, but modest sensitivity.
Authors: Amardeep Ghosh Dastidar; Iwan Harries; Giulia Pontecorboli; Vito D Bruno; Estefania De Garate; Charlie Moret; Anna Baritussio; Thomas W Johnson; Elisa McAlindon; Chiara Bucciarelli-Ducci Journal: Int J Cardiovasc Imaging Date: 2018-10-24 Impact factor: 2.357
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