[HTML][HTML] Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning

B Texier, C Hémon, P Lekieffre, E Collot, S Tahri… - Physics and Imaging in …, 2023 - Elsevier
Abstract Background and Purpose: Addressing the need for accurate dose calculation in
MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI …

CT synthesis from MRI images based on deep learning methods for MRI-only radiotherapy

Y Li, W Li, P He, J Xiong, J Xia… - … conference on medical …, 2019 - ieeexplore.ieee.org
MRI-only radiotherapy is expected to be safer and more precise compared with conventional
CT-based radiotherapy. But unlike CT, MR images is not related with electron density for …

On the effect of training database size for MR-based synthetic CT generation in the head

SIZ Estakhraji, A Pirasteh, T Bradshaw… - … Medical Imaging and …, 2023 - Elsevier
Generation of computed tomography (CT) images from magnetic resonance (MR) images
using deep learning methods has recently demonstrated promise in improving MR-guided …

Attention-guided generative adversarial network to address atypical anatomy in modality transfer

H Emami, M Dong, CK Glide-Hurst - arXiv preprint arXiv:2006.15264, 2020 - arxiv.org
Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown
rapidly in radiation therapy. However, developing class solutions for medical images that …

Intentional deep overfit learning (IDOL): A novel deep learning strategy for adaptive radiation therapy

J Chun, JC Park, S Olberg, Y Zhang, D Nguyen… - Medical …, 2022 - Wiley Online Library
Purpose Applications of deep learning (DL) are essential to realizing an effective adaptive
radiotherapy (ART) workflow. Despite the promise demonstrated by DL approaches in …

3D conditional GAN with transfer learning for pediatric MR to CT image synthesis combining adult and pediatric patient data

S Park, S Acharya, M Ladra… - Medical Imaging 2024 …, 2024 - spiedigitallibrary.org
CT image synthesis from MR images is necessary for MR-only treatment planning, MRI-
based quality assurance (QA), and treatment assessment in radiation therapy (RT). For …

MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach

S Kazemifar, S McGuire, R Timmerman… - Radiotherapy and …, 2019 - Elsevier
Purpose This study assessed the dosimetric accuracy of synthetic CT images generated
from magnetic resonance imaging (MRI) data for focal brain radiation therapy, using a deep …

[HTML][HTML] Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma …

Y Peng, S Chen, A Qin, M Chen, X Gao, Y Liu… - Radiotherapy and …, 2020 - Elsevier
Background and purpose To investigate the feasibility of synthesizing computed tomography
(CT) images from magnetic resonance (MR) images using generative adversarial networks …

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI‐guided radiation therapy

Y Dong, F Yang, J Wen, J Cai, F Zeng, M Liu… - Medical …, 2024 - Wiley Online Library
Background Cine magnetic resonance (MR) images have been used for real‐time MR
guided radiation therapy (MRgRT). However, the onboard MR systems with low‐field …

CBCT-to-CT translation using Registration-Based generative adversarial networks in patients with Head and Neck Cancer

C Suwanraksa, J Bridhikitti, T Liamsuwan… - Cancers, 2023 - mdpi.com
Simple Summary Cone-beam computed tomography (CBCT) not only plays an important
role in image-guided radiation therapy (IGRT) but also has the potential for dose calculation …