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Chun Xiang Tang

Showing results (31-40 of 41) with videos related to

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The Canadian Journal of Cardiology|November 5, 2019
Diagnostic Performance of Machine Learning Based CT-FFR in Detecting Ischemia in Myocardial Bridging and Concomitant Proximal Atherosclerotic DiseaseFan Zhou, Yi Ning Wang, U Joseph Schoepf, et al.
European Journal of Radiology|June 3, 2019
Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: A multi-center study and meta-analysisChun Xiang Tang, Yi Ning Wang, Fan Zhou, et al.
Frontiers in Cardiovascular Medicine|April 18, 2022
Diagnosis of Cardiac Amyloidosis Using a Radiomics Approach Applied to Late Gadolinium-Enhanced Cardiac Magnetic Resonance Images: A Retrospective, Multicohort, Diagnostic StudyXi Yang Zhou, Chun Xiang Tang, Ying Kun Guo, et al.
European Radiology|January 12, 2022
Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter studyYi Xue, Min Wen Zheng, Yang Hou, et al.
Journal of Thoracic Imaging|December 5, 2022
Optimal Measurement Sites of Coronary-Computed Tomography Angiography-derived Fractional Flow Reserve: The Insight From China CT-FFR StudyYan Chun Chen, Fan Zhou, Yi Ning Wang, et al.
European Radiology|March 8, 2022
Functional CAD-RADS using FFR<sub>CT</sub> on therapeutic management and prognosis in patients with coronary artery diseaseChun Xiang Tang, Hong Yan Qiao, Xiao Lei Zhang, et al.
Radiology|April 21, 2026
A Fully Automated Deep Learning Model for Quantifying Coronary Plaque at Coronary CT AngiographyQian Chen, Fan Zhou, Wei Xing, et al.
European Radiology|September 15, 2020
The effect of coronary calcification on diagnostic performance of machine learning-based CT-FFR: a Chinese multicenter studyMeng Di Jiang, Xiao Lei Zhang, Hui Liu, et al.
European Radiology|February 2, 2020
The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiographyPeng Peng Xu, Jian Hua Li, Fan Zhou, et al.
JACC. Cardiovascular Imaging|August 19, 2019
CT FFR for Ischemia-Specific CAD With a New Computational Fluid Dynamics Algorithm: A Chinese Multicenter StudyChun Xiang Tang, Chun Yu Liu, Meng Jie Lu, et al.
Pageof 5

Showing results (31-40 of 41) with videos related to

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Pageof 5
The Canadian Journal of Cardiology|November 5, 2019
Diagnostic Performance of Machine Learning Based CT-FFR in Detecting Ischemia in Myocardial Bridging and Concomitant Proximal Atherosclerotic DiseaseFan Zhou, Yi Ning Wang, U Joseph Schoepf, et al.
European Journal of Radiology|June 3, 2019
Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: A multi-center study and meta-analysisChun Xiang Tang, Yi Ning Wang, Fan Zhou, et al.
Frontiers in Cardiovascular Medicine|April 18, 2022
Diagnosis of Cardiac Amyloidosis Using a Radiomics Approach Applied to Late Gadolinium-Enhanced Cardiac Magnetic Resonance Images: A Retrospective, Multicohort, Diagnostic StudyXi Yang Zhou, Chun Xiang Tang, Ying Kun Guo, et al.
European Radiology|January 12, 2022
Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter studyYi Xue, Min Wen Zheng, Yang Hou, et al.
Journal of Thoracic Imaging|December 5, 2022
Optimal Measurement Sites of Coronary-Computed Tomography Angiography-derived Fractional Flow Reserve: The Insight From China CT-FFR StudyYan Chun Chen, Fan Zhou, Yi Ning Wang, et al.
European Radiology|March 8, 2022
Functional CAD-RADS using FFR<sub>CT</sub> on therapeutic management and prognosis in patients with coronary artery diseaseChun Xiang Tang, Hong Yan Qiao, Xiao Lei Zhang, et al.
Radiology|April 21, 2026
A Fully Automated Deep Learning Model for Quantifying Coronary Plaque at Coronary CT AngiographyQian Chen, Fan Zhou, Wei Xing, et al.
European Radiology|September 15, 2020
The effect of coronary calcification on diagnostic performance of machine learning-based CT-FFR: a Chinese multicenter studyMeng Di Jiang, Xiao Lei Zhang, Hui Liu, et al.
European Radiology|February 2, 2020
The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiographyPeng Peng Xu, Jian Hua Li, Fan Zhou, et al.
JACC. Cardiovascular Imaging|August 19, 2019
CT FFR for Ischemia-Specific CAD With a New Computational Fluid Dynamics Algorithm: A Chinese Multicenter StudyChun Xiang Tang, Chun Yu Liu, Meng Jie Lu, et al.
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