3D Unsupervised deep learning method for magnetic resonance imaging-to-computed tomography synthesis in prostate radiotherapy.

Fiche publication

Date publication

juillet 2024

Journal

Physics and imaging in radiation oncology

Auteurs

Membres identifiés du Cancéropôle Est :
Dr BESSIERES Igor

Tous les auteurs :
Texier B, Hémon C, Queffélec A, Dowling J, Bessieres I, Greer P, Acosta O, Boue-Rafle A, de Crevoisier R, Lafond C, Castelli J, Barateau A, Nunes JC

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/39161728

Résumé

Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center's learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation.