Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion

[1]  G. Pontone,et al.  Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis , 2020, BioMed research international.

[2]  A. Laghi,et al.  Artificial intelligence in cardiac radiology , 2020, La radiologia medica.

[3]  D. Andreini,et al.  Sequential Strategy Including FFRCT Plus Stress-CTP Impacts on Management of Patients with Stable Chest Pain: The Stress-CTP RIPCORD Study , 2020, Journal of clinical medicine.

[4]  Marco Valgimigli,et al.  2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. , 2019, European heart journal.

[5]  D. Andreini,et al.  Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA. , 2019, Atherosclerosis.

[6]  D. Andreini,et al.  Diagnostic performance of non-invasive imaging for stable coronary artery disease: A meta-analysis. , 2019, International journal of cardiology.

[7]  Jeroen J. Bax,et al.  Determinants of Rejection Rate for Coronary CT Angiography Fractional Flow Reserve Analysis. , 2019, Radiology.

[8]  Charles A. Taylor,et al.  Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics. , 2019, JACC. Cardiovascular imaging.

[9]  Yuanfang Guan,et al.  Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. , 2018, European heart journal.

[10]  Mauro Pepi,et al.  Incremental Diagnostic Value of Stress Computed Tomography Myocardial Perfusion With Whole-Heart Coverage CT Scanner in Intermediate- to High-Risk Symptomatic Patients Suspected of Coronary Artery Disease. , 2018, JACC. Cardiovascular imaging.

[11]  D. Andreini,et al.  Stress Computed Tomography Perfusion Versus Fractional Flow Reserve CT Derived in Suspected Coronary Artery Disease: The PERFECTION Study. , 2019, JACC. Cardiovascular imaging.

[12]  Tim Leiner,et al.  Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis , 2018, European Radiology.

[13]  D. Andreini,et al.  Impact of a New Adaptive Statistical Iterative Reconstruction (ASIR)-V Algorithm on Image Quality in Coronary Computed Tomography Angiography. , 2018, Academic radiology.

[14]  G. Pontone,et al.  Training in cardiac computed tomography: EACVI certification process. , 2018, European heart journal cardiovascular Imaging.

[15]  P. Carrascosa,et al.  Myocardial CT perfusion imaging for ischemia detection. , 2017, Cardiovascular diagnosis and therapy.

[16]  J. Leipsic,et al.  SCCT guidelines for the performance and acquisition of coronary computed tomographic angiography: A report of the society of Cardiovascular Computed Tomography Guidelines Committee: Endorsed by the North American Society for Cardiovascular Imaging (NASCI). , 2016, Journal of cardiovascular computed tomography.

[17]  D. Andreini,et al.  The New Frontier of Cardiac Computed Tomography Angiography: Fractional Flow Reserve and Stress Myocardial Perfusion , 2016, Current Treatment Options in Cardiovascular Medicine.

[18]  D. Andreini,et al.  Rationale and design of the PERFECTION (comparison between stress cardiac computed tomography PERfusion versus Fractional flow rEserve measured by Computed Tomography angiography In the evaluation of suspected cOroNary artery disease) prospective study. , 2016, Journal of cardiovascular computed tomography.

[19]  Guanglei Xiong,et al.  Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest , 2015, Medical Image Anal..

[20]  Xiantao Song,et al.  Fractional fl ow reserve versus angiography for guiding percutaneous coronary intervention: a meta-analysis , 2015 .

[21]  S. Kim,et al.  Adenosine-stress dynamic myocardial perfusion imaging using 128-slice dual-source CT in patients with normal body mass indices: effect of tube voltage, tube current, and iodine concentration on image quality and radiation dose , 2014, The International Journal of Cardiovascular Imaging.

[22]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[23]  D. Andreini,et al.  A long-term prognostic value of CT angiography and exercise ECG in patients with suspected CAD. , 2013, JACC. Cardiovascular imaging.

[24]  Wei Guo,et al.  Aligning Coronary Anatomy and Myocardial Perfusion Territories: An Algorithm for the CORE320 Multicenter Study , 2012, Circulation. Cardiovascular imaging.

[25]  Laura Mauri,et al.  2011 ACCF/AHA/SCAI Guideline for Percutaneous Coronary Intervention: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines and the Society for Cardiovascular Angiography and Interventions. , 2011, Circulation.

[26]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[27]  U. Siebert,et al.  Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. , 2009, The New England journal of medicine.

[28]  Simon Capewell,et al.  Coronary heart disease mortality among young adults in the U.S. from 1980 through 2002: concealed leveling of mortality rates. , 2007, Journal of the American College of Cardiology.

[29]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. , 2002, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[30]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. , 2002, Circulation.

[31]  R. Frye,et al.  A reporting system on patients evaluated for coronary artery disease. Report of the Ad Hoc Committee for Grading of Coronary Artery Disease, Council on Cardiovascular Surgery, American Heart Association. , 1975, Circulation.

[32]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2022 .