Super resolution deep learning reconstruction for coronary CT angiography: A structured phantom study.
Fiche publication
Date publication
juin 2024
Journal
European journal of radiology open
Auteurs
Membres identifiés du Cancéropôle Est :
Pr OHANA Mickaël
Tous les auteurs :
Higaki T, Tatsugami F, Ohana M, Nakamura Y, Kawashita I, Awai K
Lien Pubmed
Résumé
Super-resolution deep-learning-based reconstruction: SR-DLR is a newly developed and clinically available deep-learning-based image reconstruction method that can improve the spatial resolution of CT images. The image quality of the output from non-linear image reconstructions, such as DLR, is known to vary depending on the structure of the object being scanned, and a simple phantom cannot explicitly evaluate the clinical performance of SR-DLR. This study aims to accurately investigate the quality of the images reconstructed by SR-DLR by utilizing a structured phantom that simulates the human anatomy in coronary CT angiography.
Mots clés
Computed tomography, Image quality, Structured phantom, Super-resolution deep-learning-based reconstruction
Référence
Eur J Radiol Open. 2024 06;12:100570