CT-Based Pelvic T1-Weighted MR Image Synthesis Using UNet, UNet++ and Cycle-Consistent Generative Adversarial Network (Cycle-GAN)

CT-Based Pelvic T1-Weighted MR Image Synthesis Using UNet, UNet++ and Cycle-Consistent Generative Adversarial Network (Cycle-GAN)

BackgroundComputed tomography (CT) and magnetic resonance imaging (MRI) are the mainstay imaging modalities in radiotherapy planning. In MR-Linac treatment, manual annotation of organs-at-risk (OARs) and clinical volumes requires a significant clinician interaction and is a major challenge. Currentl...

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Bibliographic Details
Main Authors: Reza Kalantar, Christina Messiou, Jessica M. Winfield, Alexandra Renn, Arash Latifoltojar, Kate Downey, Aslam Sohaib, Susan Lalondrelle, Dow-Mu Koh, Matthew D. Blackledge
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Oncology
Subjects:
convolutional neural network (CNN)
generative adversarial network (GAN)
medical image synthesis
radiotherapy planning
magnetic resonance imaging (MRI)
computed tomography (CT)
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2021.665807/full

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