Connection

Carlo De Cecco to Coronary Artery Disease

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This is a "connection" page, showing publications Carlo De Cecco has written about Coronary Artery Disease.
Carlo De Cecco
Connection Strength
8.604
Coronary Artery Disease
  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: 0.414
  2. Prognostic Value of Stress Dynamic Myocardial Perfusion CT in a Multicenter Population With Known or Suspected Coronary Artery Disease. AJR Am J Roentgenol. 2017 Apr; 208(4):761-769.
    View in: PubMed
    Score: 0.368
  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: 0.365
  4. CT myocardial perfusion: state of the science. Minerva Cardioangiol. 2017 Jun; 65(3):252-264.
    View in: PubMed
    Score: 0.364
  5. Approaches to ultra-low radiation dose coronary artery calcium scoring based on 3rd generation dual-source CT: A phantom study. Eur J Radiol. 2016 Jan; 85(1):39-47.
    View in: PubMed
    Score: 0.337
  6. Impact of an advanced image-based monoenergetic reconstruction algorithm on coronary stent visualization using third generation dual-source dual-energy CT: a phantom study. Eur Radiol. 2016 Jun; 26(6):1871-8.
    View in: PubMed
    Score: 0.334
  7. Absolute Versus Relative Myocardial Blood Flow by Dynamic CT Myocardial Perfusion Imaging in Patients With Anatomic Coronary Artery Disease. AJR Am J Roentgenol. 2015 Jul; 205(1):W67-72.
    View in: PubMed
    Score: 0.329
  8. 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: 0.321
  9. Incremental value of pharmacological stress cardiac dual-energy CT over coronary CT angiography alone for the assessment of coronary artery disease in a high-risk population. AJR Am J Roentgenol. 2014 Jul; 203(1):W70-7.
    View in: PubMed
    Score: 0.307
  10. Image quality and radiation dose of low tube voltage 3rd generation dual-source coronary CT angiography in obese patients: a phantom study. Eur Radiol. 2014 Jul; 24(7):1643-50.
    View in: PubMed
    Score: 0.304
  11. Coronary artery computed tomography scanning. Circulation. 2014 Mar 25; 129(12):1341-5.
    View in: PubMed
    Score: 0.301
  12. Global cardiac evaluation without heart rate control: preliminary experience with dual source CT (DSCT). Minerva Cardioangiol. 2008 Dec; 56(6):587-97.
    View in: PubMed
    Score: 0.209
  13. Discordance Between Coronary Artery Calcium Area and Density Predicts Long-Term Atherosclerotic Cardiovascular Disease Risk. JACC Cardiovasc Imaging. 2022 Nov; 15(11):1929-1940.
    View in: PubMed
    Score: 0.134
  14. Evolving Role of Calcium Density in Coronary Artery Calcium Scoring and?Atherosclerotic Cardiovascular Disease Risk. JACC Cardiovasc Imaging. 2022 09; 15(9):1648-1662.
    View in: PubMed
    Score: 0.132
  15. Latent Tuberculosis Infection and Subclinical Coronary Atherosclerosis in Peru and Uganda. Clin Infect Dis. 2021 11 02; 73(9):e3384-e3390.
    View in: PubMed
    Score: 0.128
  16. The Feasibility, Tolerability, Safety, and Accuracy of Low-radiation Dynamic Computed Tomography Myocardial Perfusion Imaging With Regadenoson Compared With Single-photon Emission Computed Tomography. J Thorac Imaging. 2021 Nov 01; 36(6):345-352.
    View in: PubMed
    Score: 0.128
  17. Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study. Eur J Radiol. 2021 Jan; 134:109428.
    View in: PubMed
    Score: 0.120
  18. Artificial intelligence in cardiac radiology. Radiol Med. 2020 Nov; 125(11):1186-1199.
    View in: PubMed
    Score: 0.118
  19. 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: 0.117
  20. Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging. 2020 Dec; 36(12):2429-2439.
    View in: PubMed
    Score: 0.116
  21. 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: 0.115
  22. Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography. J Thorac Imaging. 2020 May; 35 Suppl 1:S49-S57.
    View in: PubMed
    Score: 0.115
  23. Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion. J Thorac Imaging. 2020 May; 35 Suppl 1:S58-S65.
    View in: PubMed
    Score: 0.115
  24. Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA. Atherosclerosis. 2020 02; 294:25-32.
    View in: PubMed
    Score: 0.112
  25. 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: 0.109
  26. 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: 0.109
  27. Design of CTP-PRO study (impact of stress Cardiac computed Tomography myocardial Perfusion on downstream resources and PROgnosis in patients with suspected or known coronary artery disease: A multicenter international study). Int J Cardiol. 2019 10 01; 292:253-257.
    View in: PubMed
    Score: 0.108
  28. Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning. J Cardiovasc Comput Tomogr. 2019 Sep - Oct; 13(5):274-280.
    View in: PubMed
    Score: 0.107
  29. Prognostic value of CT myocardial perfusion imaging and CT-derived fractional flow reserve for major adverse cardiac events in patients with coronary artery disease. J Cardiovasc Comput Tomogr. 2019 May - Jun; 13(3):26-33.
    View in: PubMed
    Score: 0.106
  30. Progression of coronary atherosclerotic plaque burden and relationship with adverse cardiovascular event in asymptomatic diabetic patients. BMC Cardiovasc Disord. 2019 02 11; 19(1):39.
    View in: PubMed
    Score: 0.106
  31. Intermodel disagreement of myocardial blood flow estimation from dynamic CT perfusion imaging. Eur J Radiol. 2019 Jan; 110:175-180.
    View in: PubMed
    Score: 0.104
  32. Current and future applications of CT coronary calcium assessment. Expert Rev Cardiovasc Ther. 2018 Jun; 16(6):441-453.
    View in: PubMed
    Score: 0.100
  33. Machine learning in cardiac CT: Basic concepts and contemporary data. J Cardiovasc Comput Tomogr. 2018 May - Jun; 12(3):192-201.
    View in: PubMed
    Score: 0.100
  34. High-pitch low-voltage CT coronary artery calcium scoring with tin filtration: accuracy and radiation dose reduction. Eur Radiol. 2018 Jul; 28(7):3097-3104.
    View in: PubMed
    Score: 0.098
  35. Heavily Calcified Coronary Arteries: Advanced Calcium Subtraction Improves Luminal Visualization and Diagnostic Confidence in Dual-Energy Coronary Computed Tomography Angiography. Invest Radiol. 2018 02; 53(2):103-109.
    View in: PubMed
    Score: 0.098
  36. Iterative beam-hardening correction with advanced modeled iterative reconstruction in low voltage CT coronary calcium scoring with tin filtration: Impact on coronary artery calcium quantification and image quality. J Cardiovasc Comput Tomogr. 2017 Sep - Oct; 11(5):354-359.
    View in: PubMed
    Score: 0.095
  37. CT angiography to evaluate coronary artery disease and revascularization requirement before trans-catheter aortic valve replacement. J Cardiovasc Comput Tomogr. 2017 Sep - Oct; 11(5):338-346.
    View in: PubMed
    Score: 0.094
  38. 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: 0.093
  39. 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: 0.091
  40. Accuracy and Radiation Dose Reduction Using Low-Voltage Computed Tomography Coronary Artery Calcium Scoring With Tin Filtration. Am J Cardiol. 2017 02 15; 119(4):675-680.
    View in: PubMed
    Score: 0.090
  41. 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: 0.090
  42. Correction Factors for CT Coronary Artery Calcium Scoring Using Advanced Modeled Iterative Reconstruction Instead of Filtered Back Projection. Acad Radiol. 2016 12; 23(12):1480-1489.
    View in: PubMed
    Score: 0.089
  43. 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: 0.089
  44. 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: 0.088
  45. Myocardial perfusion imaging with dual energy CT. Eur J Radiol. 2016 Oct; 85(10):1914-1921.
    View in: PubMed
    Score: 0.088
  46. Dynamic CT myocardial perfusion imaging identifies early perfusion abnormalities in diabetes and hypertension: Insights from a multicenter registry. J Cardiovasc Comput Tomogr. 2016 Jul-Aug; 10(4):301-8.
    View in: PubMed
    Score: 0.088
  47. 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: 0.086
  48. Mammographic detection of breast arterial calcification as an independent predictor of coronary atherosclerotic disease in a single ethnic cohort of African American women. Atherosclerosis. 2015 Sep; 242(1):218-21.
    View in: PubMed
    Score: 0.082
  49. Prognostic value of epicardial fat volume measurements by computed tomography: a systematic review of the literature. Eur Radiol. 2015 Nov; 25(11):3372-81.
    View in: PubMed
    Score: 0.081
  50. Influence of technical parameters on epicardial fat volume quantification at cardiac CT. Eur J Radiol. 2015 Jun; 84(6):1062-7.
    View in: PubMed
    Score: 0.081
  51. CT myocardial perfusion imaging. AJR Am J Roentgenol. 2015 Mar; 204(3):487-97.
    View in: PubMed
    Score: 0.080
  52. Computed tomographic assessment of coronary artery disease: state-of-the-art imaging techniques. Radiol Clin North Am. 2015 Mar; 53(2):271-85.
    View in: PubMed
    Score: 0.080
  53. Dual-source CT imaging to plan transcatheter aortic valve replacement: accuracy for diagnosis of obstructive coronary artery disease. Radiology. 2015 Apr; 275(1):80-8.
    View in: PubMed
    Score: 0.079
  54. Feasibility of prospectively ECG-triggered high-pitch coronary CT angiography with 30 mL iodinated contrast agent at 70 kVp: initial experience. Eur Radiol. 2014 Jul; 24(7):1537-46.
    View in: PubMed
    Score: 0.076
  55. Monoenergetic extrapolation of cardiac dual energy CT for artifact reduction. Acta Radiol. 2015 Apr; 56(4):413-8.
    View in: PubMed
    Score: 0.075
  56. First-arterial-pass dual-energy CT for assessment of myocardial blood supply: do we need rest, stress, and delayed acquisition? Comparison with SPECT. Radiology. 2014 Mar; 270(3):708-16.
    View in: PubMed
    Score: 0.073
  57. Automated quantification of epicardial adipose tissue using CT angiography: evaluation of a prototype software. Eur Radiol. 2014 Feb; 24(2):519-26.
    View in: PubMed
    Score: 0.073
  58. Preoperative coronary risk assessment with dual-source CT in patients undergoing noncoronary cardiac surgery. Radiol Med. 2010 Oct; 115(7):1028-37.
    View in: PubMed
    Score: 0.057
  59. The Journal of Cardiovascular Computed Tomography: 2020 Year in review. J Cardiovasc Comput Tomogr. 2021 Mar-Apr; 15(2):180-189.
    View in: PubMed
    Score: 0.030
  60. Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis. Biomed Res Int. 2020; 2020:6649410.
    View in: PubMed
    Score: 0.030
  61. 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: 0.028
  62. Novel imaging biomarkers: epicardial adipose tissue evaluation. Br J Radiol. 2020 Sep 01; 93(1113):20190770.
    View in: PubMed
    Score: 0.028
  63. Coronary computed tomographic angiography in clinical practice: state of the art. Radiol Clin North Am. 2015 Mar; 53(2):287-96.
    View in: PubMed
    Score: 0.020
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.