Impact of the sampling rate of dynamic myocardial computed tomography perfusion on the quantitative assessment of myocardial blood flow


      • CT-MBF with 2RR sampling has similar quantification and diagnostic accuracy as that of 1RR sampling.
      • CT-MBF with 3RR and greater sampling impair diagnostic accuracy for detecting myocardial perfusion abnormalities.
      • Reducing the sampling rate of dynamic CTP has influence on CT-MBF calculation especially in normal myocardia.



      The relationship between shot-to-shot sampling rates for dynamic myocardial computed tomography perfusion (CTP) and robustness of CTP-derived myocardial blood flow (CT-MBF) is debatable. We retrospectively investigated the influence of a reduced sampling rate for dynamic CTP on CT-MBF computation and diagnostic performance for detecting myocardial perfusion abnormalities.


      Pharmacological stress dynamic whole-heart CTP was performed in 120 patients suspected with coronary artery disease. Dynamic CTP data were obtained for 30 continuous heartbeats during the R-peak to R-peak (1RR) interval on electrocardiography. Three additional datasets were created with sub-sampling acquisitions every 2, 3, and 4 heartbeats from the original dataset as interval times of 2RR, 3RR, and 4RR, respectively. CT-MBF was calculated using deconvolution analysis and determined as the mean value of the whole heart (global CT-MBF) and using the 16-segment model (segmental CT-MBF). The diagnostic performance of segmental CT-MBF for detecting perfusion abnormalities was compared to that of cardiac magnetic resonance imaging as the gold standard in 32 of 120 patients. These results were compared among the four CTP datasets.


      Global CT-MBFs for 1RR, 2RR, 3RR, and 4RR sampling were 1.57 ± 0.34, 1.54 ± 0.36, 1.51 ± 0.37, and 1.41 ± 0.33 mL/g/min, respectively. Areas under the receiver operating characteristic curves of segmental CT-MBF for 1RR, 2RR, 3RR, and 4RR sampling were 0.84, 0.83, 0.79, and 0.76, respectively (1RR versus [vs.] 2RR, non-significant; 1RR vs. 3RR or 4RR, p < 0.05).


      CT-MBF with 2RR sampling has similar performance with regard to quantification and detecting myocardial perfusion abnormalities as that with 1RR sampling.


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        • Hachamovitch R.
        • Di Carli M.F.
        Methods and limitations of assessing new noninvasive tests: part II: outcomes-based validation and reliability assessment of noninvasive testing.
        Circulation. 2008; 117: 2793-2801
      1. Shaw LJ, Berman DS, Maron DJ, Mancini GB, Hayes SW, Hartigan PM, et al. Optimal medical therapy with or without percutaneous coronary intervention to reduce ischemic burden: results from the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) trial nuclear substudy. Circulation 2008; 117: 1283–91. doi:

      2. Schuijf JD, Wijns W, Jukema JW, Atsma DE, de Roos A, Lamb HJ, et al. Relationship between noninvasive coronary angiography with multi-slice computed tomography and myocardial perfusion imaging. J Am Coll Cardiol 2006; 48: 2508–14. doi:

        • Meijboom W.B.
        • Van Mieghem C.A.
        • van Pelt N.
        • Weustink A.
        • Pugliese F.
        • Mollet N.R.
        • et al.
        Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina.
        J Am Coll Cardiol. 2008; 52: 636-643
      3. Cury RC, Magalhães TA, Borges AC, Shiozaki AA, Lemos PA, Júnior JS, et al. Dipyridamole stress and rest myocardial perfusion by 64-detector row computed tomography in patients with suspected coronary artery disease. Am J Cardiol 2010; 106: 310–5. doi:

        • Bettencourt N.
        • Chiribiri A.
        • Schuster A.
        • Ferreira N.
        • Sampaio F.
        • Pires-Morais G.
        • et al.
        Direct comparison of cardiac magnetic resonance and multi detector computed tomography stress-rest perfusion imaging for detection of coronary artery disease.
        J Am Coll Cardiol. 2013; 61: 1099-1107
      4. Rochitte CE, George RT, Chen MY, Arbab-Zadeh A, Dewey M, Miller JM, et al. Computed tomography angiography and perfusion to assess coronary artery stenosis causing perfusion defects by single photon emission computed tomography: the CORE320 study. Eur Heart J 2014; 35: 1120–30. doi:

        • Ho K.T.
        • Chua K.C.
        • 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: 811-820
      5. Bamberg F, Marcus RP, Becker A, Hildebrandt K, Bauner K, Schwarz F, et al. Dynamic myocardial CT perfusion imaging for evaluation of myocardial ischemia as determined by MR imaging. JACC Cardiovasc Imaging 2014; 7: 267–77. doi:

      6. Tanabe Y, Kido T, Uetani T, Kurata A, Kono T, Ogimoto A, 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: 3790–801. doi:

      7. George RT, Jerosch-Herold M, Silva C, Kitagawa K, Bluemke DA, Lima JA, et al. Quantification of myocardial perfusion using dynamic 64-detector computed tomography. Invest Radiol 2007; 42: 815–22. doi:

      8. Patel AR, Lodato JA, Chandra S, Kachenoura N, Ahmad H, Freed BH, et al. Detection of myocardial perfusion abnormalities using ultra-low radiation dose regadenoson stress multidetector computed tomography. J Cardiovasc Comput Tomogr 2011; 5: 247–54. doi:

        • Kim S.M.
        • Kim Y.N.
        • Choe Y.H.
        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: 875-884
      9. Wiesmann M, Berg S, Bohner G, Klingebiel R, Schöpf V, Stoeckelhuber BM, et al. Dose reduction in dynamic perfusion CT of the brain: effects of the scan frequency on measurements of cerebral blood flow, cerebral blood volume, and mean transit time. Eur Radiol 2008;18:2967–74. doi:

        • Wintermark M.
        • Smith W.S.
        • Ko N.U.
        • Quist M.
        • Schnyder P.
        • Dillon W.P.
        Dynamic perfusion CT: optimizing the temporal resolution and contrast volume for calculation of perfusion CT parameters in stroke patients.
        AJNR Am J Neuroradiol. 2004; 25: 720-729
      10. Kämena A, Streitparth F, Grieser C, Lehmkuhl L, Jamil B, Wojtal K, et al. Dynamic perfusion CT: optimizing the temporal resolution for the calculation of perfusion CT parameters in stroke patients. Eur J Radiol 2007;64:111–18. doi:

        • Cerqueira M.D.
        • Weissman N.J.
        • Dilsizian V.
        • Jacobs A.K.
        • Kaul S.
        • Laskey W.K.
        • 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
      11. Kido T, Nagao M, Kido T, Kurata A, Miyagawa M, Ogimoto A, et al. Stress/rest circumferential strain in non-ischemia, ischemia, and infarction–quantification by 3 Tesla tagged magnetic resonance imaging. Circ J 2013;77:1235–41.

        • Schuirmann D.L.
        On hypothesis testing to determine if the mean of a normal distribution is contained in a known interval.
        Biometrics. 1981; 37: 617
        • Robinson A.P.
        • Froese R.E.
        Model validation using equivalence tests.
        Ecol Model. 2004; 176: 349-358
        • Wellek S.
        Testing statistical hypotheses of equivalence.
        Chapman and Hall/CRC, United Kingdom2003: 284
        • Westlake W.J.
        Response to T.B.L. Kirkwood: bioequivalence testing - a need to rethink.
        Biometrics. 1981; 37: 589-594
        • Sternberg M.R.
        • Hadgu A.
        A GEE approach to estimating sensitivity and specificity and coverage properties of the confidence intervals.
        Stat Med. 2001; 20: 1529-1539
        • Zhou X.H.
        • Obuchowski N.A.
        • McClish D.K.
        Statistical methods in diagnostic medicine.
        John Wiley & Sons, New York2002: 153-154
        • Perkins N.J.
        • Schisterman E.F.
        The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve.
        Am J Epidemiol. 2006; 163: 670-675
        • NIPPON DATA80 Research Group
        Risk assessment chart for death from cardiovascular disease based on a 19-year follow-up study of a Japanese representative population.
        Circ J. 2006; 70: 1249-1255
        • Rossi A.
        • Merkus D.
        • Klotz E.
        • Mollet N.
        • de Feyter P.J.
        • Krestin G.P.
        Stress myocardial perfusion: imaging with multidetector CT.
        Radiology. 2014; 70: 25-46
      12. Huber AM, Leber V, Gramer BM, Muenzel D, Leber A, Rieber J, et al. Myocardium: dynamic versus single-shot CT perfusion imaging. Radiology 2013;269:378–86. doi:

      13. Ishida M, Kitagawa K, Ichihara T, Natsume T, Nakayama R, Nagasawa N, 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;10:207–14. doi:

        • Modgil D.
        • Bindschadler M.D.
        • Alessio A.M.
        • La Rivière P.J.
        Variable temporal sampling and tube current modulation for myocardial blood flow estimation from dose-reduced dynamic computed tomography.
        J Med Imaging (Bellingham). 2017; 4026002
      14. Di Carli M, Czernin J, Hoh CK, Gerbaudo VH, Brunken RC, Huang SC, et al. Relation among stenosis severity, myocardial blood flow, and flow reserve in patients with coronary artery disease. Circulation 1995;91:1944–51.

      15. Kajander SA, Joutsiniemi E, Saraste M, Pietilä M, Ukkonen H, Saraste A, et al. Clinical value of absolute quantification of myocardial perfusion with (15)O-water in coronary artery disease. Circ Cardiovasc Imaging 2011;4:678–84. doi:

      16. Hubbard L, Ziemer B, Lipinski J, Sadeghi B, Javan H, Groves EM, et al. Functional assessment of coronary artery disease using whole-heart dynamic computed tomographic perfusion. Circ Cardiovasc Imaging 2016;9. doi:

      17. Tomizawa N, Chou S, Fujino Y, Kamitani M, Yamamoto K, Inoh S, et al. Feasibility of dynamic myocardial CT perfusion using single-source 64-row CT. J Cardiovasc Comput Tomogr 2019;13:55–61. doi:

        • Kaufmann P.A.
        • Gnecchi-Ruscone T.
        • Yap J.T.
        • Rimoldi O.
        • Camici P.G.
        Assessment of the reproducibility of baseline and hyperemic myocardial blood flow measurements with 15O-labeled water and PET.
        J Nucl Med. 1999; 40: 1848-1856
        • Camici P.G.
        • Crea F.
        Coronary microvascular dysfunction.
        N Engl J Med. 2007; 356: 830-840