Nieman K, Balla S (2020) Dynamic CT myocardial perfusion imaging. J Cardiovasc Comput Tomogr 14:303–306. https://doi.org/10.1016/j.jcct.2019.09.003

Article  PubMed  Google Scholar 

Li Y, Dai X, Lu Z et al (2021) Diagnostic performance of quantitative, semi-quantitative, and visual analysis of dynamic CT myocardial perfusion imaging: a validation study with invasive fractional flow reserve. Eur Radiol 31:525–534. https://doi.org/10.1007/s00330-020-07145-5

Article  PubMed  Google Scholar 

Schindler TH, Fearon WF, Pelletier-Galarneau M et al (2023) Myocardial perfusion PET for the detection and reporting of coronary microvascular dysfunction. JACC Cardiovasc Imaging 16:536–548. https://doi.org/10.1016/j.jcmg.2022.12.015

Article  PubMed  Google Scholar 

Samuels BA, Shah SM, Widmer RJ et al (2023) Comprehensive Management of ANOCA, Part 1—Definition, Patient Population, and diagnosis. J Am Coll Cardiol 82:1245–1263. https://doi.org/10.1016/j.jacc.2023.06.043

Article  PubMed  Google Scholar 

Jürgens M, Schou M, Hasbak P et al (2021) Effects of Empagliflozin on myocardial Flow Reserve in patients with type 2 diabetes Mellitus: the SIMPLE Trial. J Am Heart Assoc 10. https://doi.org/10.1161/JAHA.120.020418

Yokoi T, Tanabe Y, Kido T et al (2019) Impact of the sampling rate of dynamic myocardial computed tomography perfusion on the quantitative assessment of myocardial blood flow. Clin Imaging 56:93–101. https://doi.org/10.1016/j.clinimag.2019.03.016

Article  PubMed  Google Scholar 

Møller MB, Sørgaard MH, Linde JJ et al (2021) Optimization of image sampling rate to lower the radiation dose of dynamic myocardial CT perfusion. J Cardiovasc Comput Tomogr 15:457–460. https://doi.org/10.1016/j.jcct.2021.04.001

Article  PubMed  Google Scholar 

Sliwicka O, Oostveen LJ, Swiderska Chadaj Z et al (2024) Radiation dose reduction of 50% in dynamic myocardial CT perfusion with skipped beat acquisition: a retrospective study. Acta Radiol. https://doi.org/10.1177/02841851241240446

Article  PubMed  Google Scholar 

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. https://doi.org/10.1016/j.jcmg.2018.05.021

Article  PubMed  Google Scholar 

Tomizawa N, Hayakawa Y, Inoh S et al (2015) Clinical utility of landiolol for use in coronary CT angiography. Res Rep Clin Cardiol 145. https://doi.org/10.2147/RRCC.S77559

Kikuchi Y, Oyama-Manabe N, Naya M et al (2014) Quantification of myocardial blood flow using dynamic 320-row multi-detector CT as compared with 15O-H2O PET. Eur Radiol 24:1547–1556. https://doi.org/10.1007/s00330-014-3164-3

Article  PubMed  Google Scholar 

Aickin M, Gensler H (1996) Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. Am J Public Health 86:726–728. https://doi.org/10.2105/AJPH.86.5.726

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yamaguchi S, Ichikawa Y, Takafuji M et al (2024) Usefulness of second-generation motion correction algorithm in improving delineation and reducing motion artifact of coronary computed tomography angiography. J Cardiovasc Comput Tomogr 18:281–290. https://doi.org/10.1016/j.jcct.2024.02.008

Article  PubMed  Google Scholar 

Xu Y, Yu L, Shen C et al (2021) Prevalence and disease features of myocardial ischemia with non-obstructive coronary arteries: insights from a dynamic CT myocardial perfusion imaging study. Int J Cardiol 334:142–147. https://doi.org/10.1016/j.ijcard.2021.04.055

Article  PubMed  Google Scholar 

Mordi IR (2019) Non-invasive imaging in Diabetic Cardiomyopathy. J Cardiovasc Dev Dis 6:18. https://doi.org/10.3390/jcdd6020018

Article  PubMed  PubMed Central  Google Scholar 

Di Carli MF, Janisse J, Grunberger G, Ager J (2003) Role of chronic hyperglycemia in the pathogenesis of coronary microvascular dysfunction in diabetes. J Am Coll Cardiol 41:1387–1393. https://doi.org/10.1016/S0735-1097(03)00166-9

Article  CAS  PubMed  Google Scholar 

Vliegenthart R, De Cecco CN, Wichmann JL et al (2016) Dynamic CT myocardial perfusion imaging identifies early perfusion abnormalities in diabetes and hypertension: insights from a multicenter registry. J Cardiovasc Comput Tomogr 10:301–308. https://doi.org/10.1016/j.jcct.2016.05.005

Article  PubMed  Google Scholar 

Schindler TH, Facta AD, Prior JO et al (2007) Improvement in coronary vascular dysfunction produced with euglycaemic control in patients with type 2 diabetes. Heart 93:345–349. https://doi.org/10.1136/hrt.2006.094128

Article  CAS  PubMed  Google Scholar 

Naka KK, Papathanassiou K, Bechlioulis A et al (2011) Rosiglitazone improves endothelial function in patients with type 2 diabetes treated with insulin. Diab Vasc Dis Res 8:195–201. https://doi.org/10.1177/1479164111408628

Article  PubMed  Google Scholar 

Abdelmoneim SS, Basu A, Bernier M et al (2011) Detection of myocardial microvascular disease using contrast echocardiography during adenosine stress in type 2 diabetes mellitus: prospective comparison with single-photon emission computed tomography. Diab Vasc Dis Res 8:254–261. https://doi.org/10.1177/1479164111419973

Article  PubMed  Google Scholar 

van Haare J, Kooi ME, Vink H et al (2015) Early impairment of coronary microvascular perfusion capacity in rats on a high fat diet. Cardiovasc Diabetol 14:1–15. https://doi.org/10.1186/s12933-015-0312-2

Article  CAS  Google Scholar 

Valenta I, Dilsizian V, Quercioli A et al (2012) The influence of insulin resistance, obesity, and Diabetes Mellitus on Vascular Tone and Myocardial Blood Flow. Curr Cardiol Rep 14:217–225. https://doi.org/10.1007/s11886-011-0240-z

Article  PubMed  Google Scholar 

von Scholten BJ, Hasbak P, Christensen TE et al (2016) Cardiac 82Rb PET/CT for fast and non-invasive assessment of microvascular function and structure in asymptomatic patients with type 2 diabetes. Diabetologia 59:371–378. https://doi.org/10.1007/s00125-015-3799-x

Article  CAS  Google Scholar 

Scholte AJHA, Schuijf JD, Kharagjitsingh AV et al (2009) Prevalence and predictors of an abnormal stress myocardial perfusion study in asymptomatic patients with type 2 diabetes mellitus. Eur J Nucl Med Mol Imaging 36:567–575. https://doi.org/10.1007/s00259-008-0967-y

Article  CAS  PubMed  Google Scholar 

Hosseinzadeh E, Ghodsirad M, Alirezaie T et al (2022) Assessing the prevalence and predicting factors of an abnormal gated myocardial perfusion SPECT in asymptomatic patients with type 2 diabetes. Int J Cardiovasc Imaging 38:457–464. https://doi.org/10.1007/s10554-021-02400-2

Article  CAS  PubMed  Google Scholar 

Yamasaki Y, Nakajima K, Kusuoka H et al (2010) Prognostic value of gated myocardial perfusion imaging for asymptomatic patients with type 2 diabetes. Diabetes Care 33:2320–2326. https://doi.org/10.2337/dc09-2370

Article  PubMed  PubMed Central  Google Scholar 

Mochizuki J, Nakaura T, Yoshida N et al (2022) Spectral imaging with dual-layer spectral detector computed tomography for the detection of perfusion defects in acute coronary syndrome. Heart Vessels 37:1115–1124. https://doi.org/10.1007/s00380-021-02019-2

Article  PubMed  Google Scholar 

Mukai-Yatagai N, Ohta Y, Amisaki R et al (2020) Myocardial delayed enhancement on dual-energy computed tomography: the prevalence and related factors in patients with suspicion of coronary artery disease. J Cardiol 75:302–308. https://doi.org/10.1016/j.jjcc.2019.08.004

Article  PubMed  Google Scholar 

Nishiyama H, Tanabe Y, Kido T et al (2019) Incremental diagnostic value of whole-heart dynamic computed tomography perfusion imaging for detecting obstructive coronary artery disease. J Cardiol 73:425–431. https://doi.org/10.1016/j.jjcc.2018.12.006

Article  PubMed  Google Scholar