作者
Konrad Pieszko, Aakash Shanbhag, Aditya Killekar, Robert JH Miller, Mark Lemley, Yuka Otaki, Ananya Singh, Jacek Kwiecinski, Heidi Gransar, Serge D Van Kriekinge, Paul B Kavanagh, Edward J Miller, Timothy Bateman, Joanna X Liang, Daniel S Berman, Damini Dey, Piotr J Slomka
发表日期
2023/5/1
期刊
Cardiovascular Imaging
卷号
16
期号
5
页码范围
675-687
出版商
American College of Cardiology Foundation
简介
Background
Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. CAC is also visible in computed tomographic attenuation correction (CTAC) scans, always acquired with cardiac positron emission tomographic (PET) imaging.
Objectives
The aim of this study was to develop a deep-learning (DL) model capable of fully automated CAC definition from PET CTAC scans.
Methods
The novel DL model, originally developed for video applications, was adapted to rapidly quantify CAC. The model was trained using 9,543 expert-annotated CT scans and was tested in 4,331 patients from an external cohort undergoing PET/CT imaging with major adverse cardiac events (MACEs) (follow-up 4.3 years), including same-day paired electrocardiographically gated CAC scans available in 2,737 patients. MACE risk stratification in 4 CAC …
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K Pieszko, A Shanbhag, A Killekar, RJH Miller… - Cardiovascular Imaging, 2023