Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning.
Fiche publication
Date publication
octobre 2023
Journal
Physics and imaging in radiation oncology
Auteurs
Membres identifiés du Cancéropôle Est :
Dr BESSIERES Igor
Tous les auteurs :
Texier B, Hémon C, Lekieffre P, Collot E, Tahri S, Chourak H, Dowling J, Greer P, Bessieres I, Acosta O, Boue-Rafle A, Guevelou JL, de Crevoisier R, Lafond C, Castelli J, Barateau A, Nunes JC
Lien Pubmed
Résumé
Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability. To overcome this limitation, recent studies have proposed approaches, such as multicenter training . The purpose of this work was to propose a multicenter sCT synthesis by DL, using a 2D cycle-GAN on 128 prostate cancer patients, from four different centers. Four cases were compared: monocenter cases, monocenter training and test on another center, multicenter trainings and a test on a center not included in the training and multicenter trainings with an included center in the test. Trainings were performed using 20 patients. sCT accuracy evaluation was performed using Mean Absolute Error, Mean Error and Peak-Signal-to-Noise-Ratio. Dose accuracy was assessed with gamma index and Dose Volume Histogram comparison. Qualitative, quantitative and dose results show that the accuracy of sCTs for monocenter trainings and multicenter trainings using a seen center in the test did not differ significantly. However, when the test involved an unseen center, the sCT quality was inferior. The aim of this work was to propose generalizable multicenter training for MR-to-CT synthesis. It was shown that only a few data from one center included in the training cohort allows sCT accuracy equivalent to a monocenter study.
Mots clés
Magnetic resonance imaging, Multicenter, Radiotherapy treatment planning, Synthetic-CT, cycle-GAN
Référence
Phys Imaging Radiat Oncol. 2023 10;28:100511