Miller JM, Rochitte CE, Dewey M et al (2008) Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 359:2324–2336
Article CAS PubMed Google Scholar
Schuijf JD, Wijns W, Jukema JW et al (2006) Relationship between noninvasive coronary angiography with multi-slice computed tomography and myocardial perfusion imaging. J Am Coll Cardiol 48:2508–2514
Nakamura S, Kitagawa K, Goto Y et al (2019) Incremental prognostic value of myocardial blood flow quantified with stress dynamic computed tomography perfusion imaging. JACC Cardiovasc Imaging 12:1379–1387
Kitagawa K, Goto Y, Nakamura S et al (2018) Dynamic CT perfusion imaging: state of the art. CVIA 2:38–48
Celeng C, Leiner T, Maurovich-Horvat P et al (2019) Anatomical and functional computed tomography for diagnosing hemodynamically significant coronary artery disease: a meta-analysis. JACC Cardiovasc Imaging 12:1316–1325
Kitagawa K, Nakamura S, Ota H et al (2021) Diagnostic performance of dynamic myocardial perfusion imaging using dual-source computed tomography. J Am Coll Cardiol 78:1937–1949
Mahnken AH, Klotz E, Pietsch H et al (2010) Quantitative whole heart stress perfusion CT imaging as noninvasive assessment of hemodynamics in coronary artery stenosis: preliminary animal experience. Invest Radiol 45:298–305
Bamberg F, Hinkel R, Schwarz F et al (2012) 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 47:71–77
Rossi A, Uitterdijk A, Dijkshoorn M et al (2013) 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 14:331–338
Rossi A, Wragg A, Klotz E et al (2017) Dynamic computed tomography myocardial perfusion imaging. Circ Cardiovasc Imaging 10:e005505
Cerci RJ, Arbab-Zadeh A, George RT et al (2012) Aligning coronary anatomy and myocardial perfusion territories: an algorithm for the CORE320 multicenter study. Circ Cardiovasc Imaging 5:587–595
Min JK, Shaw LJ, Devereux RB et al (2007) Prognostic value of multidetector coronary computed tomographic angiography for prediction of all-cause mortality. J Am Coll Cardiol 50:1161–1170
Haase R, Schlattmann P, Gueret P et al (2019) Diagnosis of obstructive coronary artery disease using computed tomography angiography in patients with stable chest pain depending on clinical probability and in clinically important subgroups: meta-analysis of individual patient data. BMJ 365:l1945
Article PubMed Central PubMed Google Scholar
Knuuti J, Wijns W, Saraste A et al (2019) 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes: the Task force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur Heart J 41:407–477
Bamberg F, Becker A, Schwarz F et al (2011) Detection of hemodynamically significant coronary artery stenosis: incremental diagnostic value of dynamic CT-based myocardial perfusion imaging. Radiology 260:689–698
Greif M, von Ziegler F, Bamberg F et al (2013) CT stress perfusion imaging for detection of haemodynamically relevant coronary stenosis as defined by FFR. Heart 99:1004–1011
Rossi A, Dharampal A, Wragg A et al (2014) 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 15:85–94
Lu M, Wang S, Sirajuddin A et al (2018) Dynamic stress computed tomography myocardial perfusion for detecting myocardial ischemia: a systematic review and meta-analysis. Int J Cardiol 258:325–331
Li Y, Yu M, Dai X et al (2019) Detection of hemodynamically significant coronary stenosis: CT myocardial perfusion versus machine learning CT fractional flow reserve. Radiology 293:305–314
Rossi A, Wragg A, Klotz E et al (2017) Dynamic computed tomography myocardial perfusion imaging: comparison of clinical analysis methods for the detection of vessel-specific ischemia. Circ Cardiovasc Imaging 10:e005505
Nous FM, Geisler T, Kruk MB et al (2022) Dynamic myocardial perfusion CT for the detection of hemodynamically significant coronary artery disease. JACC Cardiovasc Imaging 15:75–87
Lyu L, Pan J, Li D et al (2022) Knowledge of hyperemic myocardial blood flow in healthy subjects helps identify myocardial ischemia in patients with coronary artery disease. Front Cardiovasc Med 9:817911
Article PubMed Central PubMed Google Scholar
Yang J, Dou G, He B et al (2020) Stress myocardial blood flow ratio by dynamic CT perfusion identifies hemodynamically significant CAD. JACC Cardiovasc Imaging 13:966–976
Danad I, Raijmakers PG, Appelman YE et al (2012) 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 39:102–112
Liga R, Rovai D, Sampietro T et al (2013) Insulin resistance is a major determinant of myocardial blood flow impairment in anginal patients. Eur J Nucl Med Mol Imaging 40:1905–1913
Wichmann JL, Meinel FG, Schoepf UJ et al (2015) Absolute versus relative myocardial blood flow by dynamic CT myocardial perfusion imaging in patients with anatomic coronary artery disease. AJR Am J Roentgenol 205:W67–W72
Kono AK, Coenen A, Lubbers M et al (2014) Relative myocardial blood flow by dynamic computed tomographic perfusion imaging predicts hemodynamic significance of coronary stenosis better than absolute blood flow. Invest Radiol 49:801–807
Rochitte CE, George RT, Chen MY et al (2014) 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 35:1120–1130
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