260 related articles for article (PubMed ID: 36168935)
1. Development of an unsupervised cycle contrastive unpaired translation network for MRI-to-CT synthesis.
Wang J; Yan B; Wu X; Jiang X; Zuo Y; Yang Y
J Appl Clin Med Phys; 2022 Nov; 23(11):e13775. PubMed ID: 36168935
[TBL] [Abstract][Full Text] [Related]
2. Compensation cycle consistent generative adversarial networks (Comp-GAN) for synthetic CT generation from MR scans with truncated anatomy.
Zhao Y; Wang H; Yu C; Court LE; Wang X; Wang Q; Pan T; Ding Y; Phan J; Yang J
Med Phys; 2023 Jul; 50(7):4399-4414. PubMed ID: 36698291
[TBL] [Abstract][Full Text] [Related]
3. Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy.
Gao L; Xie K; Wu X; Lu Z; Li C; Sun J; Lin T; Sui J; Ni X
Radiat Oncol; 2021 Oct; 16(1):202. PubMed ID: 34649572
[TBL] [Abstract][Full Text] [Related]
4. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy.
Liang X; Chen L; Nguyen D; Zhou Z; Gu X; Yang M; Wang J; Jiang S
Phys Med Biol; 2019 Jun; 64(12):125002. PubMed ID: 31108465
[TBL] [Abstract][Full Text] [Related]
5. Channel-wise attention enhanced and structural similarity constrained cycleGAN for effective synthetic CT generation from head and neck MRI images.
Gong C; Huang Y; Luo M; Cao S; Gong X; Ding S; Yuan X; Zheng W; Zhang Y
Radiat Oncol; 2024 Mar; 19(1):37. PubMed ID: 38486193
[TBL] [Abstract][Full Text] [Related]
6. Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy.
Fu J; Singhrao K; Cao M; Yu V; Santhanam AP; Yang Y; Guo M; Raldow AC; Ruan D; Lewis JH
Biomed Phys Eng Express; 2020 Jan; 6(1):015033. PubMed ID: 33438621
[TBL] [Abstract][Full Text] [Related]
7. Streaking artifact reduction for CBCT-based synthetic CT generation in adaptive radiotherapy.
Gao L; Xie K; Sun J; Lin T; Sui J; Yang G; Ni X
Med Phys; 2023 Feb; 50(2):879-893. PubMed ID: 36183234
[TBL] [Abstract][Full Text] [Related]
8. Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network.
Kaushik SS; Bylund M; Cozzini C; Shanbhag D; Petit SF; Wyatt JJ; Menzel MI; Pirkl C; Mehta B; Chauhan V; Chandrasekharan K; Jonsson J; Nyholm T; Wiesinger F; Menze B
Phys Med Biol; 2023 Sep; 68(19):. PubMed ID: 37567235
[No Abstract] [Full Text] [Related]
9. Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy.
Olberg S; Zhang H; Kennedy WR; Chun J; Rodriguez V; Zoberi I; Thomas MA; Kim JS; Mutic S; Green OL; Park JC
Med Phys; 2019 Sep; 46(9):4135-4147. PubMed ID: 31309586
[TBL] [Abstract][Full Text] [Related]
10. MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.
Lei Y; Harms J; Wang T; Liu Y; Shu HK; Jani AB; Curran WJ; Mao H; Liu T; Yang X
Med Phys; 2019 Aug; 46(8):3565-3581. PubMed ID: 31112304
[TBL] [Abstract][Full Text] [Related]
11. Patch-based generative adversarial neural network models for head and neck MR-only planning.
Klages P; Benslimane I; Riyahi S; Jiang J; Hunt M; Deasy JO; Veeraraghavan H; Tyagi N
Med Phys; 2020 Feb; 47(2):626-642. PubMed ID: 31733164
[TBL] [Abstract][Full Text] [Related]
12. Improving generalization in MR-to-CT synthesis in radiotherapy by using an augmented cycle generative adversarial network with unpaired data.
Brou Boni KND; Klein J; Gulyban A; Reynaert N; Pasquier D
Med Phys; 2021 Jun; 48(6):3003-3010. PubMed ID: 33772814
[TBL] [Abstract][Full Text] [Related]
13. CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation.
Liu J; Yan H; Cheng H; Liu J; Sun P; Wang B; Mao R; Du C; Luo S
Quant Imaging Med Surg; 2021 Dec; 11(12):4820-4834. PubMed ID: 34888192
[TBL] [Abstract][Full Text] [Related]
14. MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method.
Liu Y; Lei Y; Wang T; Kayode O; Tian S; Liu T; Patel P; Curran WJ; Ren L; Yang X
Br J Radiol; 2019 Aug; 92(1100):20190067. PubMed ID: 31192695
[TBL] [Abstract][Full Text] [Related]
15. Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model.
Pan S; Abouei E; Wynne J; Chang CW; Wang T; Qiu RLJ; Li Y; Peng J; Roper J; Patel P; Yu DS; Mao H; Yang X
Med Phys; 2024 Apr; 51(4):2538-2548. PubMed ID: 38011588
[TBL] [Abstract][Full Text] [Related]
16. Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning.
Peng Y; Chen S; Qin A; Chen M; Gao X; Liu Y; Miao J; Gu H; Zhao C; Deng X; Qi Z
Radiother Oncol; 2020 Sep; 150():217-224. PubMed ID: 32622781
[TBL] [Abstract][Full Text] [Related]
17. Unsupervised pseudo CT generation using heterogenous multicentric CT/MR images and CycleGAN: Dosimetric assessment for 3D conformal radiotherapy.
Jabbarpour A; Mahdavi SR; Vafaei Sadr A; Esmaili G; Shiri I; Zaidi H
Comput Biol Med; 2022 Apr; 143():105277. PubMed ID: 35123139
[TBL] [Abstract][Full Text] [Related]
18. Toward MR-only proton therapy planning for pediatric brain tumors: Synthesis of relative proton stopping power images with multiple sequence MRI and development of an online quality assurance tool.
Wang C; Uh J; Patni T; Merchant T; Li Y; Hua CH; Acharya S
Med Phys; 2022 Mar; 49(3):1559-1570. PubMed ID: 35075670
[TBL] [Abstract][Full Text] [Related]
19. Improvement of megavoltage computed tomography image quality for adaptive helical tomotherapy using cycleGAN-based image synthesis with small datasets.
Lee D; Jeong SW; Kim SJ; Cho H; Park W; Han Y
Med Phys; 2021 Oct; 48(10):5593-5610. PubMed ID: 34418109
[TBL] [Abstract][Full Text] [Related]
20. Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients.
Tie X; Lam SK; Zhang Y; Lee KH; Au KH; Cai J
Med Phys; 2020 Apr; 47(4):1750-1762. PubMed ID: 32012292
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]