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Dynamic Myocardial CT Perfusion Imaging

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Book cover CT of the Heart

Part of the book series: Contemporary Medical Imaging ((CMI))

Abstract

The few patient studies focusing on dynamic CTMPI for myocardial ischemia detection show promising results. Absolute quantification of perfusion parameters offers great potential, not only in the diagnosis of myocardial ischemia but potentially also in the detection of early signs of reduced myocardial blood flow as well as the diagnosis of microvascular disease and three-vessel disease. With the advent of new dose reduction techniques and new developments in CT systems, resulting in faster scanning times and wider detectors, clinical implementation of dynamic CTMPI becomes closer.

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References

  1. Jaarsma C, Leiner T, Bekkers SC, et al. Diagnostic performance of noninvasive myocardial perfusion imaging using single-photon emission computed tomography, cardiac magnetic resonance, and positron emission tomography imaging for the detection of obstructive coronary artery disease. J Am Coll Cardiol. 2012;59(19):1719–28. https://doi.org/10.1016/j.jacc.2011.12.040.

    Article  PubMed  Google Scholar 

  2. Schwitter J, Wacker CM, van Rossum AC, et al. MR-IMPACT: comparison of perfusion-cardiac magnetic resonance with single-photon emission computed tomography for the detection of coronary artery disease in a multicentre, multivendor, randomized trial. Eur Heart J. 2008;29(4):480–9. https://doi.org/10.1093/eurheartj/ehm617.

    Article  PubMed  Google Scholar 

  3. Parker MW, Iskandar A, Limone B, et al. Diagnostic accuracy of cardiac positron emission tomography versus single photon emission computed tomography for coronary artery disease: a bivariate meta-analysis. Circ Cardiovasc Imaging. 2012;5(6):700–7. https://doi.org/10.1161/CIRCIMAGING.112.978270.

    Article  PubMed  Google Scholar 

  4. Hulten EA, Carbonaro S, Petrillo SP, Mitchell JD, Villines TC. Prognostic value of cardiac computed tomography angiography: a systematic review and meta-analysis. J Am Coll Cardiol. 2011;57(10):1237–47. https://doi.org/10.1016/j.jacc.2010.10.011.

    Article  PubMed  Google Scholar 

  5. Pelgrim GJ, Dorrius M, Xie X, et al. The dream of a one-stop-shop: meta-analysis on myocardial perfusion CT. Eur J Radiol. 2015;84(12):2411–20. https://doi.org/10.1016/j.ejrad.2014.12.032.

    Article  PubMed  Google Scholar 

  6. Danad I, Szymonifka J, Schulman-Marcus J, Min JK. Static and dynamic assessment of myocardial perfusion by computed tomography. Eur Hear J Cardiovasc Imaging. 2016;17:836–44. https://doi.org/10.1093/ehjci/jew044.

    Article  Google Scholar 

  7. Williams MC, Newby DE. CT myocardial perfusion imaging: current status and future directions. Clin Radiol. 2016;71(8):1–11. https://doi.org/10.1016/j.crad.2016.03.006.

    Article  Google Scholar 

  8. Caruso D, Eid M, Schoepf UJ, et al. Dynamic CT myocardial perfusion imaging. Eur J Radiol. 2016;85:1893. https://doi.org/10.1016/j.ejrad.2016.07.017.

    Article  PubMed  Google Scholar 

  9. Vliegenthart R, De Cecco CN, Wichmann JL, et al. Dynamic CT myocardial perfusion imaging identifies early perfusion abnormalities in diabetes and hypertension: insights from a multicenter registry. J Cardiovasc Comput Tomogr. 2016;10(4):301–8. https://doi.org/10.1016/j.jcct.2016.05.005.

    Article  PubMed  Google Scholar 

  10. George RT, Jerosch-Herold M, Silva C, et al. Quantification of myocardial perfusion using dynamic 64-detector computed tomography. Investig Radiol. 2007;42(12):815–22. https://doi.org/10.1097/RLI.0b013e318124a884.

    Article  Google Scholar 

  11. Christian TF, Frankish ML, Sisemoore JH, et al. Myocardial perfusion imaging with first-pass computed tomographic imaging: measurement of coronary flow reserve in an animal model of regional hyperemia. J Nucl Cardiol. 2010;17(4):625–30. https://doi.org/10.1007/s12350-010-9206-6.

    Article  PubMed  Google Scholar 

  12. So A, Hsieh J, Li J-Y, Hadway J, Kong H-F, Lee T-Y. Quantitative myocardial perfusion measurement using CT perfusion: a validation study in a porcine model of reperfused acute myocardial infarction. Int J Cardiovasc Imaging. 2012;28(5):1237–48. https://doi.org/10.1007/s10554-011-9927-x.

    Article  PubMed  Google Scholar 

  13. Mahnken AH, Klotz E, Pietsch H, et al. Quantitative whole heart stress perfusion CT imaging as noninvasive assessment of hemodynamics in coronary artery stenosis: preliminary animal experience. Investig Radiol. 2010;45(6):298–305. https://doi.org/10.1097/RLI.0b013e3181dfa3cf.

    Article  Google Scholar 

  14. Bamberg F, Hinkel R, Schwarz F, Sandner TA, Baloch E, Marcus R, Becker A, Kupatt C, Wintersperger BJ, Johnson TR, Theisen DKM. 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. Investig Radiol. 2012;47(1):71–7.

    Article  Google Scholar 

  15. Rossi A, Uitterdijk A, Dijkshoorn M, et al. Quantification of myocardial blood flow by adenosine-stress CT perfusion imaging in pigs during various degrees of stenosis correlates well with coronary artery blood flow and fractional flow reserve. Eur Heart J Cardiovasc Imaging. 2013;14(4):331–8. https://doi.org/10.1093/ehjci/jes150.

    Article  PubMed  Google Scholar 

  16. Ishida M, Kitagawa K, Ichihara T, et al. Underestimation of myocardial blood flow by dynamic perfusion CT: explanations by two-compartment model analysis and limited temporal sampling of dynamic CT. J Cardiovasc Comput Tomogr. 2016;10:1–8. https://doi.org/10.1016/j.jcct.2016.01.008.

    Article  Google Scholar 

  17. Motwani M, Fairbairn TA, Larghat A, et al. Systolic versus diastolic acquisition in myocardial perfusion MR imaging. Radiology. 2012;262(3):816–23. https://doi.org/10.1148/radiol.11111549.

    Article  PubMed  Google Scholar 

  18. Motwani M, Kidambi A, Sourbron S, et al. Quantitative three-dimensional cardiovascular magnetic resonance myocardial perfusion imaging in systole and diastole at 3.0 T. Heart. 2013;99:A56–7. https://doi.org/10.1186/1532-429X-16-19.

    Article  Google Scholar 

  19. Rossi A, Merkus D, Klotz E, Mollet N, de Feyter PJ, Krestin GP. Stress myocardial perfusion: imaging with multidetector CT. Radiology. 2014;270(1):25–46. https://doi.org/10.1148/radiol.13112739.

    Article  PubMed  Google Scholar 

  20. Einstein AJ. Effects of radiation exposure from cardiac imaging: how good. J Am College Cardiol. 2012;59(6):553–65. https://doi.org/10.1016/j.jacc.2011.08.079.Effects.

    Article  Google Scholar 

  21. Yang DH, Kim Y-H, Roh JH, et al. Diagnostic performance of on-site CT-derived fractional flow reserve versus CT perfusion. Eur Heart J Cardiovasc Imaging. 2016;18:432. https://doi.org/10.1093/ehjci/jew094.

    Article  Google Scholar 

  22. Baxa J, Hromádka M, Šedivý J, et al. Regadenoson-stress dynamic myocardial perfusion improves diagnostic performance of CT angiography in assessment of intermediate coronary artery stenosis in asymptomatic patients. Biomed Res Int. 2015;2015:105629. https://doi.org/10.1155/2015/105629.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Weininger M, Schoepf UJ, Ramachandra A, et al. Adenosine-stress dynamic real-time myocardial perfusion CT and adenosine-stress first-pass dual-energy myocardial perfusion CT for the assessment of acute chest pain: initial results. Eur J Radiol. 2012;81(12):3703–10. https://doi.org/10.1016/j.ejrad.2010.11.022.

    Article  PubMed  Google Scholar 

  24. Wang Y, Qin L, Shi X, et al. Adenosine-stress dynamic myocardial perfusion imaging with second-generation dual-source CT: comparison with conventional catheter coronary angiography and SPECT nuclear myocardial perfusion imaging. Am J Roentgenol. 2012;198(3):521–9. https://doi.org/10.2214/AJR.11.7830.

    Article  Google Scholar 

  25. Bastarrika G, Ramos-Duran L, Rosenblum MA, Kang DK, Rowe GW, Schoepf UJ. Adenosine-stress dynamic myocardial CT perfusion imaging: initial clinical experience. Investig Radiol. 2010;45(6):306–13. https://doi.org/10.1097/RLI.0b013e3181dfa2f2.

    Article  CAS  Google Scholar 

  26. 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. 2016:1–12. http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L608199060.

  27. Wichmann JL, Meinel FG, Schoepf UJ, et al. Semiautomated global quantification of left ventricular myocardial perfusion at stress dynamic ct: diagnostic accuracy for detection of territorial myocardial perfusion deficits compared to visual assessment. Acad Radiol. 2016;23(4):429–37. https://doi.org/10.1016/j.acra.2015.12.005.

    Article  PubMed  Google Scholar 

  28. Wichmann JL, Meinel FG, Schoepf UJ, et al. Absolute versus relative myocardial blood flow by dynamic CT myocardial perfusion imaging in patients with anatomic coronary artery disease. Am J Roentgenol. 2015;205(1):W67–72. https://doi.org/10.2214/AJR.14.14087.

    Article  Google Scholar 

  29. Bamberg F, Marcus RP, Becker A, et al. Dynamic myocardial CT perfusion imaging for evaluation of myocardial ischemia as determined by MR imaging. JACC Cardiovasc Imaging. 2014;7(3):267–77. https://doi.org/10.1016/j.jcmg.2013.06.008.

    Article  PubMed  Google Scholar 

  30. Ebersberger U, Marcus RP, Schoepf UJ, et al. Dynamic CT myocardial perfusion imaging: performance of 3D semi-automated evaluation software. Eur Radiol. 2014;24(1):191–9. https://doi.org/10.1007/s00330-013-2997-5.

    Article  PubMed  Google Scholar 

  31. Kono AK, Coenen A, Lubbers M, et al. Relative myocardial blood flow by dynamic computed tomographic perfusion imaging predicts hemodynamic significance of coronary stenosis better than absolute blood flow. Investig Radiol. 2014;49(12):801–7. https://doi.org/10.1097/RLI.0000000000000087.

    Article  Google Scholar 

  32. Rossi A, Dharampal A, Wragg A, et al. Diagnostic performance of hyperaemic myocardial blood flow index obtained by dynamic computed tomography: does it predict functionally significant coronary lesions? Eur Heart J Cardiovasc Imaging. 2014;15(1):85–94. https://doi.org/10.1093/ehjci/jet133.

    Article  PubMed  Google Scholar 

  33. Huber AM, Leber V, Gramer BM, et al. Myocardium: dynamic versus single-shot CT perfusion imaging. Radiology. 2013;269(2):1–8. https://doi.org/10.1148/radiol.13121441.

    Article  Google Scholar 

  34. Greif M, von Ziegler F, Bamberg F, et al. CT stress perfusion imaging for detection of haemodynamically relevant coronary stenosis as defined by FFR. Heart. 2013;99(14):1004–11. https://doi.org/10.1136/heartjnl-2013-303794.

    Article  PubMed  Google Scholar 

  35. So A, Wisenberg G, Islam A, et al. Non-invasive assessment of functionally relevant coronary artery stenoses with quantitative CT perfusion: preliminary clinical experiences. Eur Radiol. 2012;22(1):39–50. https://doi.org/10.1007/s00330-011-2260-x.

    Article  PubMed  Google Scholar 

  36. Bamberg F, Becker A, Schwarz F, et al. Detection of hemodynamically significant coronary artery stenosis : incremental diagnostic value of dynamic CT-based. Radiology. 2011;260(3):689–98. https://doi.org/10.1148/radiol.11110638/-/DC1.

    Article  PubMed  Google Scholar 

  37. Ho KT, Chua KC, Klotz E, Panknin C. Stress and rest dynamic myocardial perfusion imaging by evaluation of complete time-attenuation curves with dual-source CT. JACC Cardiovasc Imaging. 2010;3(8):811–20. https://doi.org/10.1016/j.jcmg.2010.05.009.

    Article  PubMed  Google Scholar 

  38. Kido T, Kurata A, Higashino H, Inoue Y. Quantification of regional myocardial blood flow using. Circ J. 2008;72:1086–91.

    Article  PubMed  Google Scholar 

  39. Kim SM, Kim YN, Choe YH. Adenosine-stress dynamic myocardial perfusion imaging using 128-slice dual-source CT: optimization of the CT protocol to reduce the radiation dose. Int J Cardiovasc Imaging. 2013;29(4):875–84. https://doi.org/10.1007/s10554-012-0138-x.

    Article  PubMed  Google Scholar 

  40. Deseive S, Pugliese Md F, Meave A, et al. Image quality and radiation dose of a prospectively electrocardiography-triggered high-pitch data acquisition strategy for coronary CT angiography: the multicenter, randomized PROTECTION IV study. J Cardiovasc Comput Tomogr. 2015;9:278–85. https://doi.org/10.1016/j.jcct.2015.03.001.

    Article  PubMed  Google Scholar 

  41. Gordic S, Desbiolles L, Sedlmair M, et al. Optimizing radiation dose by using advanced modelled iterative reconstruction in high-pitch coronary CT angiography. Eur Radiol. 2016;26(2):459–68. https://doi.org/10.1007/s00330-015-3862-5.

    Article  PubMed  Google Scholar 

  42. Einstein AJ, Moser KW, Thompson RC, Cerqueira MD, Henzlova MJ. Radiation dose to patients from cardiac diagnostic imaging. Circulation. 2007;116(11):1290–305. https://doi.org/10.1161/CIRCULATIONAHA.107.688101.

    Article  PubMed  Google Scholar 

  43. Danad I, Raijmakers PG, Appelman YE, et al. Hybrid imaging using quantitative H215O PET and CT-based coronary angiography for the detection of coronary artery disease. J Nucl Med. 2013;54(1):55–63. https://doi.org/10.2967/jnumed.112.104687.

    Article  CAS  PubMed  Google Scholar 

  44. Fujita M, Kitagawa K, Ito T, et al. Dose reduction in dynamic CT stress myocardial perfusion imaging: comparison of 80-kV/370-mAs and 100-kV/300-mAs protocols. Eur Radiol. 2014;24(3):748–55. https://doi.org/10.1007/s00330-013-3063-z.

    Article  PubMed  Google Scholar 

  45. Gramer BM, Muenzel D, Leber V, et al. Impact of iterative reconstruction on CNR and SNR in dynamic myocardial perfusion imaging in an animal model. Eur Radiol. 2012;22(12):2654–61. https://doi.org/10.1007/s00330-012-2525-z.

    Article  CAS  PubMed  Google Scholar 

  46. Bindschadler M, Modgil D, Branch KR, La Riviere PJ, Alessio AM. Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT. Phys Med Biol. 2008;141(4):520–9. https://doi.org/10.1016/j.surg.2006.10.010.Use.

    Article  Google Scholar 

  47. Zoghbi GJ, Dorfman TA, Iskandrian AE. The effects of medications on myocardial perfusion. J Am Coll Cardiol. 2008;52(6):401–16. https://doi.org/10.1016/j.jacc.2008.04.035.

    Article  CAS  PubMed  Google Scholar 

  48. Machecourt J, Longère P, Fagret D, et al. Prognostic value of thallium-201 single-photon emission computed tomographic myocardial perfusion imaging according to extent of myocardial defect. Study in 1,926 patients with foilow-up at 33 months. J Am Coll Cardiol. 1994;23(5):1096–106. https://doi.org/10.1016/0735-1097(94)90597-5.

    Article  CAS  PubMed  Google Scholar 

  49. Cerqueira MD, Weissman NJ, Dilsizian V, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation. 2002;105:539–42.

    Article  PubMed  Google Scholar 

  50. Meinel FG, Ebersberger U, Schoepf UJ, et al. Global quantification of left ventricular myocardial perfusion at dynamic CT: feasibility in a multicenter patient population. Am J Roentgenol. 2014;203(2):174–80. https://doi.org/10.2214/AJR.13.12328.

    Article  Google Scholar 

  51. Ingrisch M, Sourbron S. Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer. J Pharmacokinet Pharmacodyn. 2013;40(3, SI):281–300. https://doi.org/10.1007/s10928-013-9315-3.

    Article  PubMed  Google Scholar 

  52. Al-Saadi N, Nagel E, Gross M, et al. Noninvasive detection of myocardial ischemia from perfusion reserve based on cardiovascular magnetic resonance. Circulation. 2000;101(12):1379–83.

    Article  CAS  PubMed  Google Scholar 

  53. Al-Saadi N, Nagel E, Gross M, et al. Improvement of myocardial perfusion reserve early after coronary intervention: assessment with cardiac magnetic resonance imaging. J Am Coll Cardiol. 2000;36(5):1557–64.

    Article  CAS  PubMed  Google Scholar 

  54. Schwitter J, Nanz D, Kneifel S, et al. Assessment of myocardial perfusion in coronary artery disease by magnetic resonance: a comparison with positron emission tomography and coronary angiography. Circulation. 2001;103(18):2230–5.

    Article  CAS  PubMed  Google Scholar 

  55. Pelgrim G, Handayani A, Dijkstra H, et al. Quantitative myocardial perfusion with dynamic contrast-enhanced imaging in MRI and CT : theoretical models and current implementation. Biomed Res Int. 2016;2016:1.

    Article  Google Scholar 

  56. Lee TY. Functional CT: physiological models. Trends Biotechnol. 2002;20(8):3–10. https://doi.org/10.1016/S0167-7799(02)02035-8.

    Article  Google Scholar 

  57. Larghat AM, Maredia N, Biglands J, et al. Reproducibility of first-pass cardiovascular magnetic resonance myocardial perfusion. J Magn Reson Imaging. 2013;37(4):865–74. https://doi.org/10.1002/jmri.23889.

    Article  PubMed  Google Scholar 

  58. Gamel BJ, Katholi CR, Mesel E. Pitfalls in digital computation of the impulse response of vascular beds from indicator-dilution curves. Circ Res. 1973;32(April):516–23.

    Article  CAS  PubMed  Google Scholar 

  59. Papanastasiou G, Williams MC, Kershaw LE, et al. Measurement of myocardial blood flow by cardiovascular magnetic resonance perfusion: comparison of distributed parameter and Fermi models with single and dual bolus. J Cardiovasc Magn Reson. 2015;17:17. https://doi.org/10.1186/s12968-015-0125-1.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Futamatsu H, Wilke N, Klassen C, et al. Evaluation of cardiac magnetic resonance imaging parameters to detect anatomically and hemodynamically significant coronary artery disease. Am Heart J. 2007;154(2):298–305. https://doi.org/10.1016/j.ahj.2007.04.024.

    Article  PubMed  Google Scholar 

  61. Maredia N, Plein S, Younger JF, et al. Detection of triple vessel coronary artery disease by visual and quantitative first pass CMR myocardial perfusion imaging in the CE-MARC study. J Cardiovasc Magn Reson. 2011;13. http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L70465436:O29.

    Article  PubMed Central  Google Scholar 

  62. Sammut E, Zarinabad N, Wesolowski R, et al. Feasibility of high-resolution quantitative perfusion analysis in patients with heart failure. J Cardiovasc Magn Reson. 2015;17:13. https://doi.org/10.1186/s12968-015-0124-2.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Kim EY, Chung WJ, Sung YM, et al. Normal range and regional heterogeneity of myocardial perfusion in healthy human myocardium: assessment on dynamic perfusion CT using 128-slice dual-source CT. Int J Cardiovasc Imaging. 2014;30(SUPPL. 1):33–40. https://doi.org/10.1007/s10554-014-0432-x.

    Article  PubMed  Google Scholar 

  64. Danad I, Raijmakers PG, Appelman YE, et al. Coronary risk factors and myocardial blood flow in patients evaluated for coronary artery disease: a quantitative [15O]H2O PET/CT study. Eur J Nucl Med Mol Imaging. 2012;39(1):102–12. https://doi.org/10.1007/s00259-011-1956-0.

    Article  PubMed  Google Scholar 

  65. Schwarz F, Hinkel R, Baloch E, et al. Myocardial CT perfusion imaging in a large animal model: comparison of dynamic versus single-phase acquisitions. JACC Cardiovasc Imaging. 2013;6(12):1229–38. https://doi.org/10.1016/j.jcmg.2013.05.018.

    Article  PubMed  Google Scholar 

  66. Kajander SA, Joutsiniemi E, Saraste M, et al. Clinical value of absolute quantification of myocardial perfusion with (15)O-water in coronary artery disease. Circ Cardiovasc Imaging. 2011;4(6):678–84. https://doi.org/10.1161/CIRCIMAGING.110.960732.

    Article  PubMed  Google Scholar 

  67. Bol A, Melin JA, Vanoverschelde JL, et al. Direct comparison of [13N]ammonia and [15O]water estimates of perfusion with quantification of regional myocardial blood flow by microspheres. Circulation. 1993;87(2):512–25. https://doi.org/10.1161/01.CIR.87.2.512.

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Marly van Assen, Gert Jan Pelgrim & Rozemarijn Vliegenthart

  2. Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC, USA

    Marly van Assen & Rozemarijn Vliegenthart

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  1. Marly van Assen
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  2. Gert Jan Pelgrim
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  3. Rozemarijn Vliegenthart
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Correspondence to Rozemarijn Vliegenthart .

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  1. Department of Radiology, Center for Advanced Imaging Research (CAIR), Medical University of South Carolina, Charleston, SC, USA

    U. Joseph Schoepf

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van Assen, M., Pelgrim, G.J., Vliegenthart, R. (2019). Dynamic Myocardial CT Perfusion Imaging. In: Schoepf, U. (eds) CT of the Heart. Contemporary Medical Imaging. Humana, Totowa, NJ. https://doi.org/10.1007/978-1-60327-237-7_63

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