Connection

Carlo De Cecco to Image Processing, Computer-Assisted

This is a "connection" page, showing publications Carlo De Cecco has written about Image Processing, Computer-Assisted.
  1. A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions. Eur Radiol. 2018 Aug; 28(8):3393-3404.
    View in: PubMed
    Score: 0.469
  2. Beam-hardening in 70-kV Coronary CT angiography: Artifact reduction using an advanced post-processing algorithm. Eur J Radiol. 2018 Apr; 101:111-117.
    View in: PubMed
    Score: 0.468
  3. Virtual unenhanced imaging of the liver with third-generation dual-source dual-energy CT and advanced modeled iterative reconstruction. Eur J Radiol. 2016 Jul; 85(7):1257-64.
    View in: PubMed
    Score: 0.413
  4. Semiautomated Global Quantification of Left Ventricular Myocardial Perfusion at Stress Dynamic CT:: Diagnostic Accuracy for Detection of Territorial Myocardial Perfusion Deficits Compared to Visual Assessment. Acad Radiol. 2016 Apr; 23(4):429-37.
    View in: PubMed
    Score: 0.407
  5. Optimization of window settings for virtual monoenergetic imaging in dual-energy CT of the liver: A multi-reader evaluation of standard monoenergetic and advanced imaged-based monoenergetic datasets. Eur J Radiol. 2016 Apr; 85(4):695-9.
    View in: PubMed
    Score: 0.406
  6. State of the Art: Iterative CT Reconstruction Techniques. Radiology. 2015 Aug; 276(2):339-57.
    View in: PubMed
    Score: 0.393
  7. Performance of Automated Software in the Assessment of Segmental Left Ventricular Function in Cardiac CT: Comparison with Cardiac Magnetic Resonance. Eur Radiol. 2015 Dec; 25(12):3560-6.
    View in: PubMed
    Score: 0.386
  8. 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: 0.361
  9. Beyond the AJR: Radiomics Meets Machine Learning to Improve Outcome Prediction. AJR Am J Roentgenol. 2022 Nov; 219(5):844.
    View in: PubMed
    Score: 0.156
  10. Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease. Sci Rep. 2021 05 27; 11(1):11112.
    View in: PubMed
    Score: 0.147
  11. Artificial intelligence in cardiac radiology. Radiol Med. 2020 Nov; 125(11):1186-1199.
    View in: PubMed
    Score: 0.140
  12. Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation. Invest Radiol. 2020 09; 55(9):619-627.
    View in: PubMed
    Score: 0.140
  13. 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: 0.117
  14. Virtual Monoenergetic Imaging and Iodine Perfusion Maps Improve Diagnostic Accuracy of Dual-Energy Computed Tomography Pulmonary Angiography With Suboptimal Contrast Attenuation. Invest Radiol. 2017 11; 52(11):659-665.
    View in: PubMed
    Score: 0.115
  15. Monoenergetic Dual-energy Computed Tomographic Imaging: Cardiothoracic Applications. J Thorac Imaging. 2017 May; 32(3):151-158.
    View in: PubMed
    Score: 0.111
  16. 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: 0.110
  17. Can dual-energy computed tomography improve visualization of hypoenhancing liver lesions in portal venous phase? Assessment of advanced image-based virtual monoenergetic images. Clin Imaging. 2017 Jan - Feb; 41:118-124.
    View in: PubMed
    Score: 0.107
  18. Application of an Advanced Image-Based Virtual Monoenergetic Reconstruction of Dual Source Dual-Energy CT Data at Low keV Increases Image Quality for Routine Pancreas Imaging. J Comput Assist Tomogr. 2015 Sep-Oct; 39(5):716-20.
    View in: PubMed
    Score: 0.099
  19. Dual-energy CT of the pancreas: improved carcinoma-to-pancreas contrast with a noise-optimized monoenergetic reconstruction algorithm. Eur J Radiol. 2015 Nov; 84(11):2052-8.
    View in: PubMed
    Score: 0.098
  20. CT Evaluation of Small-Diameter Coronary Artery Stents: Effect of an Integrated Circuit Detector with Iterative Reconstruction. Radiology. 2015 Sep; 276(3):706-14.
    View in: PubMed
    Score: 0.096
  21. Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis. Biomed Res Int. 2020; 2020:6649410.
    View in: PubMed
    Score: 0.036
  22. 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: 0.027
  23. T(Rho) and magnetization transfer and INvErsion recovery (TRAMINER)-prepared imaging: A novel contrast-enhanced flow-independent dark-blood technique for the evaluation of myocardial late gadolinium enhancement in patients with myocardial infarction. J Magn Reson Imaging. 2017 05; 45(5):1429-1437.
    View in: PubMed
    Score: 0.027
  24. 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: 0.026
  25. Low-volume contrast medium protocol for comprehensive cardiac and aortoiliac CT assessment in the context of transcatheter aortic valve replacement. Acad Radiol. 2015 Sep; 22(9):1138-46.
    View in: PubMed
    Score: 0.024
  26. Accuracy of different reconstruction intervals to quantify left ventricular function and mass in cardiac computed tomography examinations. Radiologia. 2012 Sep-Oct; 54(5):432-41.
    View in: PubMed
    Score: 0.019
  27. Anatomical variations of the coeliac trunk and the mesenteric arteries evaluated with 64-row CT angiography. Radiol Med. 2007 Oct; 112(7):988-98.
    View in: PubMed
    Score: 0.014
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.