Connection

Richard Bayer to Computed Tomography Angiography

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This is a "connection" page, showing publications Richard Bayer has written about Computed Tomography Angiography.
Richard Bayer
Connection Strength
3.675
Computed Tomography Angiography
  1. 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: 0.450
  2. One-year outcomes of CCTA alone versus machine learning-based FFRCT for coronary artery disease: a single-center, prospective study. Eur Radiol. 2022 Aug; 32(8):5179-5188.
    View in: PubMed
    Score: 0.148
  3. Feasibility and Impact of the Combined Application of Coronary CT Angiography With the HEART Pathway in Patients With Suspected Acute Coronary Syndrome. Crit Pathw Cardiol. 2021 12 01; 20(4):185-191.
    View in: PubMed
    Score: 0.146
  4. Utility of Functional and Volumetric Left Atrial Parameters Derived From Preprocedural Cardiac CTA in Predicting Mortality After Transcatheter Aortic Valve Replacement. AJR Am J Roentgenol. 2022 03; 218(3):444-452.
    View in: PubMed
    Score: 0.145
  5. Coronary CT Fractional Flow Reserve before Transcatheter Aortic Valve Replacement: Clinical Outcomes. Radiology. 2022 01; 302(1):50-58.
    View in: PubMed
    Score: 0.144
  6. Non-invasive fractional flow reserve (FFRCT) in the evaluation of acute chest pain - Concepts and first experiences. Eur J Radiol. 2021 May; 138:109633.
    View in: PubMed
    Score: 0.139
  7. Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging. 2020 Dec; 36(12):2429-2439.
    View in: PubMed
    Score: 0.132
  8. More holes, more contrast? Comparing an 18-gauge non-fenestrated catheter with a 22-gauge fenestrated catheter for cardiac CT. PLoS One. 2020; 15(6):e0234311.
    View in: PubMed
    Score: 0.132
  9. Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease. Eur Radiol. 2020 Nov; 30(11):5841-5851.
    View in: PubMed
    Score: 0.131
  10. In-Hospital Cost Comparison of Triple-Rule-Out Computed Tomography Angiography Versus Standard of Care in Patients With Acute Chest Pain. J Thorac Imaging. 2020 May; 35(3):198-203.
    View in: PubMed
    Score: 0.131
  11. 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: 0.125
  12. CT FFR for Ischemia-Specific CAD With?a?New Computational Fluid Dynamics Algorithm: A Chinese Multicenter Study. JACC Cardiovasc Imaging. 2020 04; 13(4):980-990.
    View in: PubMed
    Score: 0.124
  13. Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry. JACC Cardiovasc Imaging. 2020 03; 13(3):760-770.
    View in: PubMed
    Score: 0.124
  14. Relationship Between Pregnancy Complications and Subsequent Coronary Artery Disease Assessed by Coronary Computed Tomographic Angiography in Black Women. Circ Cardiovasc Imaging. 2019 07; 12(7):e008754.
    View in: PubMed
    Score: 0.124
  15. 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: 0.119
  16. 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: 0.113
  17. 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: 0.111
  18. Coronary CT Angiography-derived Fractional Flow Reserve. Radiology. 2017 10; 285(1):17-33.
    View in: PubMed
    Score: 0.109
  19. 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: 0.109
  20. 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: 0.108
  21. CT coronary calcium scoring with tin filtration using iterative beam-hardening calcium correction reconstruction. Eur J Radiol. 2017 Jun; 91:29-34.
    View in: PubMed
    Score: 0.105
  22. Diagnostic accuracy of coronary CT angiography using 3rd-generation dual-source CT and automated tube voltage selection: Clinical application in a non-obese and obese patient population. Eur Radiol. 2017 Jun; 27(6):2298-2308.
    View in: PubMed
    Score: 0.102
  23. Low contrast medium-volume third-generation dual-source computed tomography angiography for transcatheter aortic valve replacement planning. Eur Radiol. 2017 May; 27(5):1944-1953.
    View in: PubMed
    Score: 0.101
  24. 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: 0.101
  25. Coronary CT angiography-derived quantitative markers for predicting in-stent restenosis. J Cardiovasc Comput Tomogr. 2016 Sep-Oct; 10(5):377-83.
    View in: PubMed
    Score: 0.100
  26. 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: 0.098
  27. Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement. Eur Radiol. 2022 Sep; 32(9):6008-6016.
    View in: PubMed
    Score: 0.037
  28. Additive value of epicardial adipose tissue quantification to coronary CT angiography-derived plaque characterization and CT fractional flow reserve for the prediction of lesion-specific ischemia. Eur Radiol. 2022 Jun; 32(6):4243-4252.
    View in: PubMed
    Score: 0.037
  29. Prognostic value of epicardial adipose tissue volume in combination with coronary plaque and flow assessment for the prediction of major adverse cardiac events. Eur J Radiol. 2022 Mar; 148:110157.
    View in: PubMed
    Score: 0.037
  30. Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation. Eur Radiol. 2021 Sep; 31(9):6592-6604.
    View in: PubMed
    Score: 0.035
  31. 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: 0.031
  32. 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: 0.026
Connection Strength

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