The Value of Low-Dose Dynamic Myocardial Perfusion CT for Accurate Evaluation of Microvascular Obstruction in Patients With Acute Myocardial Infarction
Mengmeng Yu1, Xiuyu Chen2, Xu Dai1, Jingwei Pan3 ... Show all
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Citation: American Journal of Roentgenology: 1-9. 10.2214/AJR.19.21305
AbstractFull TextReferencesPDFPDF PlusAdd to FavoritesPermissionsDownload Citation
ABSTRACT :
OBJECTIVE. The purpose of this study was to investigate the diagnostic performance of quantitative parameters generated from dynamic myocardial perfusion CT for assessment of acute myocardial infarction (AMI) and microvascular obstruction (MVO) using cardiac MRI as a reference standard.
SUBJECTS AND METHODS. Patients who underwent successful reperfusion treatment within 1 week after AMI between January 1, 2018, and May 31, 2018, were prospectively enrolled. All patients were referred for cardiac MRI and dynamic myocardial perfusion CT on the same day. Various quantitative parameters and late iodine enhancement (LIE) were analyzed for the evaluation of AMI and MVO using cardiac MRI findings as a reference standard.
RESULTS. Twenty-seven patients with 442 vascular segments were ultimately included in the analysis. The mean radiation doses ± SD for dynamic myocardial perfusion CT and LIE were 3.3 ± 1.1 mSv and 2.0 ± 0.6 mSv, respectively. Myocardial blood flow (MBF) was significantly lower in segments with MVO than in those without MVO and in reference segments (23.08 ± 7.95 mL/min/100 mL vs 44.60 ± 14.97 mL/min/100 mL and 75.07 ± 7.34 mL/min/100 mL; p < 0.001). According to ROC curve analysis, MBF had the largest AUC of all parameters for identifying AMI with and without MVO as determined by late gadolinium enhancement (LGE) (AUC = 0.941 and 0.996; p < 0.001). The diagnostic accuracy of MBF-based assessment for identifying MVO was 99.2%, which outperformed other quantitative parameters and LIE. We found good correlation between the AMI area and MVO area estimated by MBF and LGE (r = 0.95 and 0.99; p < 0.001).
CONCLUSION. MBF derived from dynamic myocardial perfusion CT is accurate and outperforms other quantitative parameters and LIE in diagnosis of AMI and MVO. Area of AMI and MVO can also be accurately estimated using MBF.
Keywords: acute myocardial infarction, CT, microvascular obstruction, myocardial blood flow, myocardial perfusion imaging
Based on a presentation at the European Society of Radiology 2019 annual meeting, Vienna, Austria, published in Insights Imaging 2019; 10(Suppl 1):22.
Supported by National Natural Science Foundation of China (Grant No. 81671678), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (Grant No. 20161428), Shanghai Key Discipline of Medical Imaging (No. 2017ZZ02005) and The National Key Research and Development Program of China (Grant Nos. 2016YFC1300400, 2016YFC1300402).
References
Previous section
1. Yeh RW, Sidney S, Chandra M, Sorel M, Selby JV, Go AS. Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med 2010; 362:2155–2165 [Crossref] [Medline] [Google Scholar]
2. Lerman A, Holmes DR, Herrmann J, Gersh BJ. Microcirculatory dysfunction in ST-elevation myocardial infarction: cause, consequence, or both? Eur Heart J 2007; 28:788–797 [Crossref] [Medline] [Google Scholar]
3. Britten MB, Zeiher AM, Schächinger V. Micro-vascular dysfunction in angiographically normal or mildly diseased coronary arteries predicts adverse cardiovascular long-term outcome. Coron Artery Dis 2004; 15:259–264 [Crossref] [Medline] [Google Scholar]
4. Yan AT, Gibson CM, Larose E, et al. Characterization of microvascular dysfunction after acute myocardial infarction by cardiovascular magnetic resonance first-pass perfusion and late gadolinium enhancement imaging. J Cardiovasc Magn Reson 2006; 8:831–837 [Crossref] [Medline] [Google Scholar]
5. Mather AN, Lockie T, Nagel E, et al. Appearance of microvascular obstruction on high resolution first-pass perfusion, early and late gadolinium enhancement CMR in patients with acute myocardial infarction. J Cardiovasc Magn Reson 2009; 11:33 [Crossref] [Medline] [Google Scholar]
6. Tanabe Y, Kido T, Uetani T, et al. Differentiation of myocardial ischemia and infarction assessed by dynamic computed tomography perfusion imaging and comparison with cardiac magnetic resonance and single-photon emission computed tomography. Eur Radiol 2016; 26:3790–3801 [Crossref] [Medline] [Google Scholar]
7. Tanabe Y, Kido T, Kurata A, et al. Late iodine enhancement computed tomography with image subtraction for assessment of myocardial infarction. Eur Radiol 2018; 28:1285–1292 [Crossref] [Medline] [Google Scholar]
8. Coenen A, Rossi A, Lubbers MM, et al. Integrating CT myocardial perfusion and CT-FFR in the work-up of coronary artery disease. JACC Cardiovasc Imaging 2017; 10:760–770 [Crossref] [Medline] [Google Scholar]
9. Rossi A, Wragg A, Klotz E, et al. Dynamic computed tomography myocardial perfusion imaging: comparison of clinical analysis methods for the detection of vessel-specific ischemia. Circ Cardiovasc Imaging 2017; 10:e005505 [Crossref] [Medline] [Google Scholar]
10. Pelgrim GJ, Duguay TM, Stijnen JM, et al. Analysis of myocardial perfusion parameters in an exvivo porcine heart model using third generation dual-source CT. J Cardiovasc Comput Tomogr 2017; 11:141–147 [Crossref] [Medline] [Google Scholar]
11. Dai X, Yu M, Pan J, et al. Image quality and diagnostic accuracy of coronary CT angiography derived from low-dose dynamic CT myocardial per-fusion: a feasibility study with comparison to invasive coronary angiography. Eur Radiol 2018 Nov 9 [Epub ahead of print] [Google Scholar]
12. Morsbach F, Desbiolles L, Plass A, et al. Stenosis quantification in coronary CT angiography: impact of an integrated circuit detector with iterative reconstruction. Invest Radiol 2013; 48:32–40 [Crossref] [Medline] [Google Scholar]
13. Bamberg F, Klotz E, Flohr T, et al. Dynamic myocardial stress perfusion imaging using fast dual-source CT with alternating table positions: initial experience. Eur Radiol 2010; 20:1168–1173 [Crossref] [Medline] [Google Scholar]
14. Rossi A, Merkus D, Klotz E, Mollet N, de Feyter PJ, Krestin GP. Stress myocardial perfusion: imaging with multidetector CT. Radiology 2014; 270:25–46 [Crossref] [Medline] [Google Scholar]
15. Bamberg F, Hinkel R, Schwarz F, et al. Accuracy of dynamic computed tomography adenosine stress myocardial perfusion imaging in estimating myocardial blood flow at various degrees of coronary artery stenosis using a porcine animal model. Invest Radiol 2012; 47:71–77 [Crossref] [Medline] [Google Scholar]
16. Cerqueira MD, Weissman NJ, Dilsizian V, et al.; American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation 2002; 105:539–542 [Crossref] [Medline] [Google Scholar]
17. Elias J, van Dongen IM, Hoebers LP, et al.; EXPLORE investigators. Improved recovery of regional left ventricular function after PCI of chronic total occlusion in STEMI patients: a cardiovascular magnetic resonance study of the randomized controlled EXPLORE trial. J Cardiovasc Magn Reson 2017; 19:53 [Crossref] [Medline] [Google Scholar]
18. van Kranenburg M, Magro M, Thiele H, et al. Prognostic value of microvascular obstruction and infarct size, as measured by CMR in STEMI patients. JACC Cardiovasc Imaging 2014; 7:930–939 [Crossref] [Medline] [Google Scholar]
19. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:29–36 [Crossref] [Medline] [Google Scholar]
20. Wu KC. CMR of microvascular obstruction and hemorrhage in myocardial infarction. J Cardiovasc Magn Reson 2012; 14:68 [Crossref] [Medline] [Google Scholar]
21. Hamirani YS, Wong A, Kramer CM, Salerno M. Effect of microvascular obstruction and intramyocardial hemorrhage by CMR on LV remodeling and outcomes after myocardial infarction: a systematic review and meta-analysis. JACC Cardiovasc Imaging 2014; 7:940–952 [Crossref] [Medline] [Google Scholar]
22. Kurobe Y, Kitagawa K, Ito T, et al. Myocardial delayed enhancement with dual-source CT: advantages of targeted spatial frequency filtration and image averaging over half-scan reconstruction. J Cardiovasc Comput Tomogr 2014; 8:289–298 [Crossref] [Medline] [Google Scholar]
23. Niccoli G, Scalone G, Lerman A, Crea F. Coronary microvascular obstruction in acute myocar-dial infarction. Eur Heart J 2016; 37:1024–1033 [Crossref] [Medline] [Google Scholar]
24. Danad I, Szymonifka J, Schulman-Marcus J, Min JK. Static and dynamic assessment of myocardial perfusion by computed tomography. Eur Heart J Cardiovasc Imaging 2016; 17:836–844 [Crossref] [Medline] [Google Scholar]
Address correspondence to J. Zhang (andrewssmu@msn.com).
M. Yu and X. Chen contributed equally to this study.
Read More: https://www.ajronline.org/doi/abs/10.2214/AJR.19.21305
Mengmeng Yu1, Xiuyu Chen2, Xu Dai1, Jingwei Pan3 ... Show all
Share Share
+ Affiliations:
Citation: American Journal of Roentgenology: 1-9. 10.2214/AJR.19.21305
AbstractFull TextReferencesPDFPDF PlusAdd to FavoritesPermissionsDownload Citation
ABSTRACT :
OBJECTIVE. The purpose of this study was to investigate the diagnostic performance of quantitative parameters generated from dynamic myocardial perfusion CT for assessment of acute myocardial infarction (AMI) and microvascular obstruction (MVO) using cardiac MRI as a reference standard.
SUBJECTS AND METHODS. Patients who underwent successful reperfusion treatment within 1 week after AMI between January 1, 2018, and May 31, 2018, were prospectively enrolled. All patients were referred for cardiac MRI and dynamic myocardial perfusion CT on the same day. Various quantitative parameters and late iodine enhancement (LIE) were analyzed for the evaluation of AMI and MVO using cardiac MRI findings as a reference standard.
RESULTS. Twenty-seven patients with 442 vascular segments were ultimately included in the analysis. The mean radiation doses ± SD for dynamic myocardial perfusion CT and LIE were 3.3 ± 1.1 mSv and 2.0 ± 0.6 mSv, respectively. Myocardial blood flow (MBF) was significantly lower in segments with MVO than in those without MVO and in reference segments (23.08 ± 7.95 mL/min/100 mL vs 44.60 ± 14.97 mL/min/100 mL and 75.07 ± 7.34 mL/min/100 mL; p < 0.001). According to ROC curve analysis, MBF had the largest AUC of all parameters for identifying AMI with and without MVO as determined by late gadolinium enhancement (LGE) (AUC = 0.941 and 0.996; p < 0.001). The diagnostic accuracy of MBF-based assessment for identifying MVO was 99.2%, which outperformed other quantitative parameters and LIE. We found good correlation between the AMI area and MVO area estimated by MBF and LGE (r = 0.95 and 0.99; p < 0.001).
CONCLUSION. MBF derived from dynamic myocardial perfusion CT is accurate and outperforms other quantitative parameters and LIE in diagnosis of AMI and MVO. Area of AMI and MVO can also be accurately estimated using MBF.
Keywords: acute myocardial infarction, CT, microvascular obstruction, myocardial blood flow, myocardial perfusion imaging
Based on a presentation at the European Society of Radiology 2019 annual meeting, Vienna, Austria, published in Insights Imaging 2019; 10(Suppl 1):22.
Supported by National Natural Science Foundation of China (Grant No. 81671678), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (Grant No. 20161428), Shanghai Key Discipline of Medical Imaging (No. 2017ZZ02005) and The National Key Research and Development Program of China (Grant Nos. 2016YFC1300400, 2016YFC1300402).
References
Previous section
1. Yeh RW, Sidney S, Chandra M, Sorel M, Selby JV, Go AS. Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med 2010; 362:2155–2165 [Crossref] [Medline] [Google Scholar]
2. Lerman A, Holmes DR, Herrmann J, Gersh BJ. Microcirculatory dysfunction in ST-elevation myocardial infarction: cause, consequence, or both? Eur Heart J 2007; 28:788–797 [Crossref] [Medline] [Google Scholar]
3. Britten MB, Zeiher AM, Schächinger V. Micro-vascular dysfunction in angiographically normal or mildly diseased coronary arteries predicts adverse cardiovascular long-term outcome. Coron Artery Dis 2004; 15:259–264 [Crossref] [Medline] [Google Scholar]
4. Yan AT, Gibson CM, Larose E, et al. Characterization of microvascular dysfunction after acute myocardial infarction by cardiovascular magnetic resonance first-pass perfusion and late gadolinium enhancement imaging. J Cardiovasc Magn Reson 2006; 8:831–837 [Crossref] [Medline] [Google Scholar]
5. Mather AN, Lockie T, Nagel E, et al. Appearance of microvascular obstruction on high resolution first-pass perfusion, early and late gadolinium enhancement CMR in patients with acute myocardial infarction. J Cardiovasc Magn Reson 2009; 11:33 [Crossref] [Medline] [Google Scholar]
6. Tanabe Y, Kido T, Uetani T, et al. Differentiation of myocardial ischemia and infarction assessed by dynamic computed tomography perfusion imaging and comparison with cardiac magnetic resonance and single-photon emission computed tomography. Eur Radiol 2016; 26:3790–3801 [Crossref] [Medline] [Google Scholar]
7. Tanabe Y, Kido T, Kurata A, et al. Late iodine enhancement computed tomography with image subtraction for assessment of myocardial infarction. Eur Radiol 2018; 28:1285–1292 [Crossref] [Medline] [Google Scholar]
8. Coenen A, Rossi A, Lubbers MM, et al. Integrating CT myocardial perfusion and CT-FFR in the work-up of coronary artery disease. JACC Cardiovasc Imaging 2017; 10:760–770 [Crossref] [Medline] [Google Scholar]
9. Rossi A, Wragg A, Klotz E, et al. Dynamic computed tomography myocardial perfusion imaging: comparison of clinical analysis methods for the detection of vessel-specific ischemia. Circ Cardiovasc Imaging 2017; 10:e005505 [Crossref] [Medline] [Google Scholar]
10. Pelgrim GJ, Duguay TM, Stijnen JM, et al. Analysis of myocardial perfusion parameters in an exvivo porcine heart model using third generation dual-source CT. J Cardiovasc Comput Tomogr 2017; 11:141–147 [Crossref] [Medline] [Google Scholar]
11. Dai X, Yu M, Pan J, et al. Image quality and diagnostic accuracy of coronary CT angiography derived from low-dose dynamic CT myocardial per-fusion: a feasibility study with comparison to invasive coronary angiography. Eur Radiol 2018 Nov 9 [Epub ahead of print] [Google Scholar]
12. Morsbach F, Desbiolles L, Plass A, et al. Stenosis quantification in coronary CT angiography: impact of an integrated circuit detector with iterative reconstruction. Invest Radiol 2013; 48:32–40 [Crossref] [Medline] [Google Scholar]
13. Bamberg F, Klotz E, Flohr T, et al. Dynamic myocardial stress perfusion imaging using fast dual-source CT with alternating table positions: initial experience. Eur Radiol 2010; 20:1168–1173 [Crossref] [Medline] [Google Scholar]
14. Rossi A, Merkus D, Klotz E, Mollet N, de Feyter PJ, Krestin GP. Stress myocardial perfusion: imaging with multidetector CT. Radiology 2014; 270:25–46 [Crossref] [Medline] [Google Scholar]
15. Bamberg F, Hinkel R, Schwarz F, et al. Accuracy of dynamic computed tomography adenosine stress myocardial perfusion imaging in estimating myocardial blood flow at various degrees of coronary artery stenosis using a porcine animal model. Invest Radiol 2012; 47:71–77 [Crossref] [Medline] [Google Scholar]
16. Cerqueira MD, Weissman NJ, Dilsizian V, et al.; American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation 2002; 105:539–542 [Crossref] [Medline] [Google Scholar]
17. Elias J, van Dongen IM, Hoebers LP, et al.; EXPLORE investigators. Improved recovery of regional left ventricular function after PCI of chronic total occlusion in STEMI patients: a cardiovascular magnetic resonance study of the randomized controlled EXPLORE trial. J Cardiovasc Magn Reson 2017; 19:53 [Crossref] [Medline] [Google Scholar]
18. van Kranenburg M, Magro M, Thiele H, et al. Prognostic value of microvascular obstruction and infarct size, as measured by CMR in STEMI patients. JACC Cardiovasc Imaging 2014; 7:930–939 [Crossref] [Medline] [Google Scholar]
19. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:29–36 [Crossref] [Medline] [Google Scholar]
20. Wu KC. CMR of microvascular obstruction and hemorrhage in myocardial infarction. J Cardiovasc Magn Reson 2012; 14:68 [Crossref] [Medline] [Google Scholar]
21. Hamirani YS, Wong A, Kramer CM, Salerno M. Effect of microvascular obstruction and intramyocardial hemorrhage by CMR on LV remodeling and outcomes after myocardial infarction: a systematic review and meta-analysis. JACC Cardiovasc Imaging 2014; 7:940–952 [Crossref] [Medline] [Google Scholar]
22. Kurobe Y, Kitagawa K, Ito T, et al. Myocardial delayed enhancement with dual-source CT: advantages of targeted spatial frequency filtration and image averaging over half-scan reconstruction. J Cardiovasc Comput Tomogr 2014; 8:289–298 [Crossref] [Medline] [Google Scholar]
23. Niccoli G, Scalone G, Lerman A, Crea F. Coronary microvascular obstruction in acute myocar-dial infarction. Eur Heart J 2016; 37:1024–1033 [Crossref] [Medline] [Google Scholar]
24. Danad I, Szymonifka J, Schulman-Marcus J, Min JK. Static and dynamic assessment of myocardial perfusion by computed tomography. Eur Heart J Cardiovasc Imaging 2016; 17:836–844 [Crossref] [Medline] [Google Scholar]
Address correspondence to J. Zhang (andrewssmu@msn.com).
M. Yu and X. Chen contributed equally to this study.
Read More: https://www.ajronline.org/doi/abs/10.2214/AJR.19.21305
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