A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study.
Fiche publication
Date publication
novembre 2023
Journal
Frontiers in oncology
Auteurs
Membres identifiés du Cancéropôle Est :
Dr BESSIERES Igor
Tous les auteurs :
Tahri S, Texier B, Nunes JC, Hemon C, Lekieffre P, Collot E, Chourak H, Le Guevelou J, Greer P, Dowling J, Acosta O, Bessieres I, Marage L, Boue-Rafle A, De Crevoisier R, Lafond C, Barateau A
Lien Pubmed
Résumé
For radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapy.
Mots clés
CT synthesis, MR-only radiotherapy, MRI, deep learning, dose planning
Référence
Front Oncol. 2023 11 28;13:1279750