Connection

Carlo De Cecco to Coronary Stenosis

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This is a "connection" page, showing publications Carlo De Cecco has written about Coronary Stenosis.
Carlo De Cecco
Connection Strength
4.712
Coronary Stenosis
  1. Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFRML): Impact of iterative and filtered back projection reconstruction techniques. J Cardiovasc Comput Tomogr. 2019 Nov - Dec; 13(6):331-335.
    View in: PubMed
    Score: 0.530
  2. Diagnostic accuracy of low and high tube voltage coronary CT angiography using an X-ray tube potential-tailored contrast medium injection protocol. Eur Radiol. 2018 May; 28(5):2134-2142.
    View in: PubMed
    Score: 0.498
  3. Global quantification of left ventricular myocardial perfusion at dynamic CT imaging: Prognostic value. J Cardiovasc Comput Tomogr. 2017 Jan - Feb; 11(1):16-24.
    View in: PubMed
    Score: 0.467
  4. Dynamic CT myocardial perfusion imaging. Eur J Radiol. 2016 Oct; 85(10):1893-1899.
    View in: PubMed
    Score: 0.454
  5. Beyond stenosis detection: computed tomography approaches for determining the functional relevance of coronary artery disease. Radiol Clin North Am. 2015 Mar; 53(2):317-34.
    View in: PubMed
    Score: 0.406
  6. Global quantification of left ventricular myocardial perfusion at dynamic CT: feasibility in a multicenter patient population. AJR Am J Roentgenol. 2014 Aug; 203(2):W174-80.
    View in: PubMed
    Score: 0.390
  7. Artificial intelligence in cardiac radiology. Radiol Med. 2020 Nov; 125(11):1186-1199.
    View in: PubMed
    Score: 0.151
  8. Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry. Eur J Radiol. 2019 Oct; 119:108657.
    View in: PubMed
    Score: 0.141
  9. Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome. Am J Cardiol. 2019 11 01; 124(9):1340-1348.
    View in: PubMed
    Score: 0.140
  10. Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia. Eur Radiol. 2019 May; 29(5):2378-2387.
    View in: PubMed
    Score: 0.134
  11. Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling. Radiology. 2018 Jul; 288(1):64-72.
    View in: PubMed
    Score: 0.128
  12. Noninvasive Derivation of Fractional Flow Reserve From Coronary Computed Tomographic Angiography: A Review. J Thorac Imaging. 2018 Mar; 33(2):88-96.
    View in: PubMed
    Score: 0.127
  13. Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve for Therapeutic Decision Making. Am J Cardiol. 2017 Dec 15; 120(12):2121-2127.
    View in: PubMed
    Score: 0.123
  14. Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve Based on Machine Learning for Risk Stratification of Non-Culprit Coronary Narrowings in Patients with Acute Coronary Syndrome. Am J Cardiol. 2017 Oct 15; 120(8):1260-1266.
    View in: PubMed
    Score: 0.121
  15. Coronary Computed Tomography Angiography-Derived Plaque Quantification in Patients With Acute Coronary?Syndrome. Am J Cardiol. 2017 03 01; 119(5):712-718.
    View in: PubMed
    Score: 0.116
  16. Correlation and predictive value of aortic root calcification markers with coronary artery calcification and obstructive coronary artery disease. Radiol Med. 2017 Feb; 122(2):113-120.
    View in: PubMed
    Score: 0.116
  17. Prognostic implications of coronary CT angiography-derived quantitative markers for the prediction of major adverse cardiac events. J Cardiovasc Comput Tomogr. 2016 Nov - Dec; 10(6):458-465.
    View in: PubMed
    Score: 0.114
  18. Coronary CT angiography derived morphological and functional quantitative plaque markers correlated with invasive fractional flow reserve for detecting hemodynamically significant stenosis. J Cardiovasc Comput Tomogr. 2016 May-Jun; 10(3):199-206.
    View in: PubMed
    Score: 0.110
  19. Diagnostic value of quantitative stenosis predictors with coronary CT angiography compared to invasive fractional flow reserve. Eur J Radiol. 2015 Aug; 84(8):1509-1515.
    View in: PubMed
    Score: 0.104
  20. Technical prerequisites and imaging protocols for dynamic and dual energy myocardial perfusion imaging. Eur J Radiol. 2015 Dec; 84(12):2401-10.
    View in: PubMed
    Score: 0.103
  21. Residents' performance in the interpretation of on-call "triple-rule-out" CT studies in patients with acute chest pain. Acad Radiol. 2014 Jul; 21(7):938-44.
    View in: PubMed
    Score: 0.098
  22. Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis. Biomed Res Int. 2020; 2020:6649410.
    View in: PubMed
    Score: 0.038
  23. Differences in coronary vasodilatory capacity and atherosclerosis in endurance athletes using coronary CTA and computational fluid dynamics (CFD): Comparison with a sedentary lifestyle. Eur J Radiol. 2020 Sep; 130:109168.
    View in: PubMed
    Score: 0.037
  24. Rationale and design of the quantification of myocardial blood flow using dynamic PET/CTA-fused imagery (DEMYSTIFY) to determine physiological significance of specific coronary lesions. J Nucl Cardiol. 2020 06; 27(3):1030-1039.
    View in: PubMed
    Score: 0.036
  25. Coronary CT Angiography-derived Fractional Flow Reserve. Radiology. 2017 10; 285(1):17-33.
    View in: PubMed
    Score: 0.031
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