Fiche publication
Date publication
décembre 2025
Journal
Diagnostic and interventional radiology (Ankara, Turkey)
Auteurs
Membres identifiés du Cancéropôle Est :
Pr ROY Catherine
,
Pr OHANA Mickaël
Tous les auteurs :
Graber L, Akış MZ, Séverac F, Mertz L, Akış S, Roy C, Ohana M
Lien Pubmed
Résumé
To evaluate whether deep learning reconstruction (DLR) can reduce the radiation dose in routine clinical computed tomography (CT) scans compared with iterative reconstruction (IR) while maintaining or improving image quality. The study assesses DLR's consistency and effectiveness across four distinct CT protocols-chest, head, chest-abdomen-pelvis (CAP) oncology, and lower limb CT angiography (CTA)-representing a wide range of clinical applications.
Mots clés
Deep learning reconstruction, computed tomography scan, image quality, radiation dose reduction
Référence
Diagn Interv Radiol. 2025 12 5;:
