A deep learning approach to generate synthetic CT in low field MR-guided radiotherapy for lung cases
J Lenkowicz, C Votta, M Nardini, F Quaranta… - Radiotherapy and …, 2022 - Elsevier
Introduction This study aims to apply a conditional Generative Adversarial Network (cGAN)
to generate synthetic Computed Tomography (sCT) from 0.35 Tesla Magnetic Resonance …
to generate synthetic Computed Tomography (sCT) from 0.35 Tesla Magnetic Resonance …
Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy
L Gao, K Xie, X Wu, Z Lu, C Li, J Sun, T Lin, J Sui… - Radiation Oncology, 2021 - Springer
Objective To develop high-quality synthetic CT (sCT) generation method from low-dose
cone-beam CT (CBCT) images by using attention-guided generative adversarial networks …
cone-beam CT (CBCT) images by using attention-guided generative adversarial networks …
Improving generalization in MR‐to‐CT synthesis in radiotherapy by using an augmented cycle generative adversarial network with unpaired data
Purpose MR‐to‐CT synthesis is one of the first steps in the establishment of an MRI‐only
workflow in radiotherapy. Current MR‐to‐CT synthesis methods in deep learning use …
workflow in radiotherapy. Current MR‐to‐CT synthesis methods in deep learning use …
Synthetic CT generation from weakly paired MR images using cycle-consistent GAN for MR-guided radiotherapy
Although MR-guided radiotherapy (MRgRT) is advancing rapidly, generating accurate
synthetic CT (sCT) from MRI is still challenging. Previous approaches using deep neural …
synthetic CT (sCT) from MRI is still challenging. Previous approaches using deep neural …
[HTML][HTML] Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma …
Background and purpose To investigate the feasibility of synthesizing computed tomography
(CT) images from magnetic resonance (MR) images using generative adversarial networks …
(CT) images from magnetic resonance (MR) images using generative adversarial networks …
Multi‐sequence MR image‐based synthetic CT generation using a generative adversarial network for head and neck MRI‐only radiotherapy
M Qi, Y Li, A Wu, Q Jia, B Li, W Sun, Z Dai, X Lu… - Medical …, 2020 - Wiley Online Library
Purpose The purpose of this study is to investigate the effect of different magnetic resonance
(MR) sequences on the accuracy of deep learning‐based synthetic computed tomography …
(MR) sequences on the accuracy of deep learning‐based synthetic computed tomography …
Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review
Purpose In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its
superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the …
superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the …
Compensation cycle consistent generative adversarial networks (Comp‐GAN) for synthetic CT generation from MR scans with truncated anatomy
Background MR scans used in radiotherapy can be partially truncated due to the limited field
of view (FOV), affecting dose calculation accuracy in MR‐based radiation treatment …
of view (FOV), affecting dose calculation accuracy in MR‐based radiation treatment …
MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks
Purpose Automated synthetic computed tomography (sCT) generation based on magnetic
resonance imaging (MRI) images would allow for MRI‐only based treatment planning in …
resonance imaging (MRI) images would allow for MRI‐only based treatment planning in …
Deep generative model for synthetic-CT generation with uncertainty predictions
MR-only radiation treatment planning is attractive due to the superior soft tissue definition of
MRI as compared to CT, and the elimination of the uncertainty introduced by CT-MRI …
MRI as compared to CT, and the elimination of the uncertainty introduced by CT-MRI …