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Future of nuclear cardiology is bright: Promise of cardiac PET/CT and artificial intelligence

Journal of nuclear cardiology, 2022-04, Vol.29 (2), p.389-391

Slomka, Piotr

2022

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Do we need dedicated cardiac SPECT systems?

Journal of nuclear cardiology, 2021-08, Vol.28 (4), p.1331-1333

Slomka, Piotr

2021

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Recent Advances and Future Progress in PET Instrumentation

Seminars in nuclear medicine, 2016, Vol.46 (1), p.5-19

2016

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Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review

Journal of the American College of Cardiology, 2019-03, Vol.73 (11), p.1317-1335

2019

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Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

European heart journal, 2019-06, Vol.40 (24), p.1975-1986

2019

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Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning

JACC. Cardiovascular imaging, 2018-07, Vol.11 (7), p.1000-1009

2018

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Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines)

Journal of Nuclear Medicine, 2022-09, Vol.63 (9), p.1288-1299

2022

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Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging

Scientific reports, 2021-07, Vol.11 (1), p.14490-14490, Article 14490

2021

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Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT

IEEE transactions on medical imaging, 2018-08, Vol.37 (8), p.1835-1846

2018

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Single Photon Emission Computed Tomography (SPECT) Myocardial Perfusion Imaging Guidelines: Instrumentation, Acquisition, Processing, and Interpretation

Journal of nuclear cardiology, 2018-10, Vol.25 (5), p.1784-1846

2018

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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 study

Cardiovascular research, 2020-12, Vol.116 (14), p.2216-2225

2020

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Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography

European heart journal cardiovascular imaging, 2019-06, Vol.20 (6), p.636-643

2019

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Clinical Quantification of Myocardial Blood Flow Using PET: Joint Position Paper of the SNMMI Cardiovascular Council and the ASNC

Journal of Nuclear Medicine, 2018-02, Vol.59 (2), p.273-293

2018

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Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study

Journal of Nuclear Medicine, 2019-05, Vol.60 (5), p.664-670

2019

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Solid-State Detector SPECT Myocardial Perfusion Imaging

Journal of Nuclear Medicine, 2019-09, Vol.60 (9), p.1194-1204

2019

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Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study

JACC. Cardiovascular imaging, 2018-11, Vol.11 (11), p.1654-1663

2018

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Cardiac PET/MR: Are sophisticated attenuation correction techniques necessary for clinical routine assessments?

Journal of nuclear cardiology, 2021-10, Vol.28 (5), p.2205-2206

2021

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Machine Learning Adds to Clinical and CAC Assessments in Predicting 10-Year CHD and CVD Deaths

JACC. Cardiovascular imaging, 2021-03, Vol.14 (3), p.615-625

2021

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Myocardial blood flow: Is motion correction necessary?

Journal of nuclear cardiology, 2021-08, Vol.28 (4), p.1347-1348

2021

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Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

European journal of nuclear medicine and molecular imaging, 2021-05, Vol.48 (5), p.1399-1413

2021

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Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects

Circulation. Cardiovascular imaging, 2020-02, Vol.13 (2), p.e009829-e009829

2020

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New Cardiac Cameras: Single-Photon Emission CT and PET

Seminars in nuclear medicine, 2014-07, Vol.44 (4), p.232-251

2014

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Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US

Radiology, 2010-11, Vol.257 (2), p.516

2010

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Future of nuclear cardiology is bright: Promise of cardiac PET/CT and artificial intelligence

Do we need dedicated cardiac SPECT systems?

Recent Advances and Future Progress in PET Instrumentation

Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning

Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines)

Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging

Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT

Single Photon Emission Computed Tomography (SPECT) Myocardial Perfusion Imaging Guidelines: Instrumentation, Acquisition, Processing, and Interpretation

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 study

Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography

Clinical Quantification of Myocardial Blood Flow Using PET: Joint Position Paper of the SNMMI Cardiovascular Council and the ASNC

Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study

Low-Attenuation Noncalcified Plaque on Coronary Computed Tomography Angiography Predicts Myocardial Infarction: Results From the Multicenter SCOT-HEART Trial (Scottish Computed Tomography of the HEART)

Solid-State Detector SPECT Myocardial Perfusion Imaging

Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study

Cardiac PET/MR: Are sophisticated attenuation correction techniques necessary for clinical routine assessments?

Machine Learning Adds to Clinical and CAC Assessments in Predicting 10-Year CHD and CVD Deaths

Myocardial blood flow: Is motion correction necessary?

Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects

New Cardiac Cameras: Single-Photon Emission CT and PET

Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US

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