Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning

Autor: Thomas Caramella, Imad Bousaid, Jean-Yves Airaud, Zoubir M Bensalah, Nicolas Gogin, Caroline Dam Hieu, Mario Viti, Yann Diascorn, Mickaël Ohana, Nathalie Lassau, Bassam Abdallah, Umit Gencer, Elie Mousseaux, Marc-Samir Guillot, Luc Nicodème, Hugues Talbot, Magloire Mekukosokeng

Publikováno v: Diagnostic and Interventional Imaging
Diagnostic and Interventional Imaging, 2021, 102 (11), pp.683-690. ⟨10.1016/j.diii.2021.05.004⟩
Diagnostic and Interventional Imaging, Elsevier, 2021, 102 (11), pp.683-690. ⟨10.1016/j.diii.2021.05.004⟩

International audience; Rationale and Objectives: The purpose of this study was to develop and validate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from non-enhanced ECG-gated computed tomography (CT) card

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d7b0ff053ecd33aab060f10b8742e726
https://pubmed.ncbi.nlm.nih.gov/34099435