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Sebastien Cadet

Showing results (21-30 of 67) with videos related to

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JACC. Cardiovascular Imaging|August 31, 2020
Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control StudyAndrew Lin, Márton Kolossváry, Jeremy Yuvaraj, et al.
Journal of Nuclear Cardiology : Official Publication of the American Society of Nuclear Cardiology|May 29, 2020
Quantitative Assessment of Cardiac Hypermetabolism and Perfusion for Diagnosis of Cardiac SarcoidosisRobert J H Miller, Sebastien Cadet, Payam Pournazari, et al.
Radiology. Cardiothoracic Imaging|November 1, 2023
Quantification of Low-Attenuation Plaque Burden from Coronary CT Angiography: A Head-to-Head Comparison with Near-Infrared Spectroscopy Intravascular USHiroki Tanisawa, Hidenari Matsumoto, Sebastien Cadet, et al.
Journal of Nuclear Cardiology : Official Publication of the American Society of Nuclear Cardiology|June 28, 2018
Optimization of reconstruction and quantification of motion-corrected coronary PET-CTMhairi K Doris, Yuka Otaki, Sandeep K Krishnan, et al.
Radiology. Artificial Intelligence|February 25, 2020
Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter StudyFrederic Commandeur, Markus Goeller, Aryabod Razipour, et al.
Clinical Research in Cardiology : Official Journal of the German Cardiac Society|May 10, 2020
Noncalcified plaque burden quantified from coronary computed tomography angiography improves prediction of side branch occlusion after main vessel stenting in bifurcation lesions: results from the CT-PRECISION registryKajetan Grodecki, Sebastien Cadet, Adam D Staruch, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine|September 15, 2018
Three-Hour Delayed Imaging Improves Assessment of Coronary <sup>18</sup>F-Sodium Fluoride PETJacek Kwiecinski, Daniel S Berman, Sang-Eun Lee, et al.
JACC. Cardiovascular Imaging|May 5, 2022
Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography AngiographyAndrew Lin, Márton Kolossváry, Sebastien Cadet, et al.
American Journal of Preventive Cardiology|February 3, 2025
Coronary plaque characteristics quantified by artificial intelligence-enabled plaque analysis: Insights from a multi-ethnic asymptomatic US populationGuadalupe Flores Tomasino, Caroline Park, Kajetan Grodecki, et al.
Cardiovascular Research|December 20, 2019
Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective studyFrederic Commandeur, Piotr J Slomka, Markus Goeller, et al.
Pageof 7

Showing results (21-30 of 67) with videos related to

Sort By:
Pageof 7
JACC. Cardiovascular Imaging|August 31, 2020
Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control StudyAndrew Lin, Márton Kolossváry, Jeremy Yuvaraj, et al.
Journal of Nuclear Cardiology : Official Publication of the American Society of Nuclear Cardiology|May 29, 2020
Quantitative Assessment of Cardiac Hypermetabolism and Perfusion for Diagnosis of Cardiac SarcoidosisRobert J H Miller, Sebastien Cadet, Payam Pournazari, et al.
Radiology. Cardiothoracic Imaging|November 1, 2023
Quantification of Low-Attenuation Plaque Burden from Coronary CT Angiography: A Head-to-Head Comparison with Near-Infrared Spectroscopy Intravascular USHiroki Tanisawa, Hidenari Matsumoto, Sebastien Cadet, et al.
Journal of Nuclear Cardiology : Official Publication of the American Society of Nuclear Cardiology|June 28, 2018
Optimization of reconstruction and quantification of motion-corrected coronary PET-CTMhairi K Doris, Yuka Otaki, Sandeep K Krishnan, et al.
Radiology. Artificial Intelligence|February 25, 2020
Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter StudyFrederic Commandeur, Markus Goeller, Aryabod Razipour, et al.
Clinical Research in Cardiology : Official Journal of the German Cardiac Society|May 10, 2020
Noncalcified plaque burden quantified from coronary computed tomography angiography improves prediction of side branch occlusion after main vessel stenting in bifurcation lesions: results from the CT-PRECISION registryKajetan Grodecki, Sebastien Cadet, Adam D Staruch, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine|September 15, 2018
Three-Hour Delayed Imaging Improves Assessment of Coronary <sup>18</sup>F-Sodium Fluoride PETJacek Kwiecinski, Daniel S Berman, Sang-Eun Lee, et al.
JACC. Cardiovascular Imaging|May 5, 2022
Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography AngiographyAndrew Lin, Márton Kolossváry, Sebastien Cadet, et al.
American Journal of Preventive Cardiology|February 3, 2025
Coronary plaque characteristics quantified by artificial intelligence-enabled plaque analysis: Insights from a multi-ethnic asymptomatic US populationGuadalupe Flores Tomasino, Caroline Park, Kajetan Grodecki, et al.
Cardiovascular Research|December 20, 2019
Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective studyFrederic Commandeur, Piotr J Slomka, Markus Goeller, et al.
Pageof 7