Open AccessDeep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality
Author(s) -
Angélique Bernard,
PierreOlivier Comby,
Brivaël Lemogne,
Karim Haioun,
F. Ricolfi,
Olivier Chevallier,
Romaric Loffroy
Publication year - 2020
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
eISSN - 2223-4292
pISSN - 2223-4306
DOI - 10.21037/qims-20-626
Subject(s) - medicine , image quality , iterative reconstruction , nuclear medicine , radiology , angiography , radiation dose , computed tomography angiography , stroke (engine) , contrast to noise ratio , effective dose (radiation) , image noise , radon transform , cardiac imaging , artificial intelligence , computer science , image (mathematics) , mechanical engineering , engineering
To assess the radiation dose and image quality of cardiac computed tomography angiography (CCTA) in an acute stroke imaging protocol using a deep learning reconstruction (DLR) method compared to a hybrid iterative reconstruction algorithm.
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