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

Background and purpose: 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 d...

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Bibliographic Details

Published in:	Physics and Imaging in Radiation Oncology
Main Authors:	Blanche Texier, Cédric Hémon, Adélie Queffélec, Jason Dowling, Igor Bessieres, Peter Greer, Oscar Acosta, Adrien Boue-Rafle, Renaud de Crevoisier, Caroline Lafond, Joël Castelli, Anaïs Barateau, Jean-Claude Nunes
Format:	Article
Language:	English
Published:	Elsevier 2024-07-01
Subjects:	cGAN Synthetic CT Unsupervised learning Perceptual loss
Online Access:	http://www.sciencedirect.com/science/article/pii/S2405631624000824