483 related articles for article (PubMed ID: 35354124)
1. A two-step method to improve image quality of CBCT with phantom-based supervised and patient-based unsupervised learning strategies.
Liu Y; Chen X; Zhu J; Yang B; Wei R; Xiong R; Quan H; Liu Y; Dai J; Men K
Phys Med Biol; 2022 Apr; 67(8):. PubMed ID: 35354124
[No Abstract] [Full Text] [Related]
2. 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]
3. 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]
4. Improving CBCT quality to CT level using deep learning with generative adversarial network.
Zhang Y; Yue N; Su MY; Liu B; Ding Y; Zhou Y; Wang H; Kuang Y; Nie K
Med Phys; 2021 Jun; 48(6):2816-2826. PubMed ID: 33259647
[TBL] [Abstract][Full Text] [Related]
5. Generating synthesized computed tomography from CBCT using a conditional generative adversarial network for head and neck cancer patients.
Zhang Y; Ding SG; Gong XC; Yuan XX; Lin JF; Chen Q; Li JG
Technol Cancer Res Treat; 2022; 21():15330338221085358. PubMed ID: 35262422
[No Abstract] [Full Text] [Related]
6. 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]
7. Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy.
Wang H; Liu X; Kong L; Huang Y; Chen H; Ma X; Duan Y; Shao Y; Feng A; Shen Z; Gu H; Kong Q; Xu Z; Zhou Y
Strahlenther Onkol; 2023 May; 199(5):485-497. PubMed ID: 36688953
[TBL] [Abstract][Full Text] [Related]
8. CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma.
Jihong C; Kerun Q; Kaiqiang C; Xiuchun Z; Yimin Z; Penggang B
Sci Rep; 2023 Apr; 13(1):6624. PubMed ID: 37095147
[TBL] [Abstract][Full Text] [Related]
9. Improvement of Image Quality of Cone-beam CT Images by Three-dimensional Generative Adversarial Network.
Hase T; Nakao M; Imanishi K; Nakamura M; Matsuda T
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():2843-2846. PubMed ID: 34891840
[TBL] [Abstract][Full Text] [Related]
10. Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network.
Yuan N; Rao S; Chen Q; Sensoy L; Qi J; Rong Y
Med Phys; 2022 May; 49(5):3263-3277. PubMed ID: 35229904
[TBL] [Abstract][Full Text] [Related]
11. Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy.
Szmul A; Taylor S; Lim P; Cantwell J; Moreira I; Zhang Y; D'Souza D; Moinuddin S; Gaze MN; Gains J; Veiga C
Phys Med Biol; 2023 May; 68(10):. PubMed ID: 36996837
[No Abstract] [Full Text] [Related]
12. Transformer CycleGAN with uncertainty estimation for CBCT based synthetic CT in adaptive radiotherapy.
Rusanov B; Hassan GM; Reynolds M; Sabet M; Rowshanfarzad P; Bucknell N; Gill S; Dass J; Ebert M
Phys Med Biol; 2024 Jan; 69(3):. PubMed ID: 38198726
[No Abstract] [Full Text] [Related]
13. Synthetic CT generation from CBCT images via unsupervised deep learning.
Chen L; Liang X; Shen C; Nguyen D; Jiang S; Wang J
Phys Med Biol; 2021 May; 66(11):. PubMed ID: 34061043
[TBL] [Abstract][Full Text] [Related]
14. Multiresolution residual deep neural network for improving pelvic CBCT image quality.
Wu W; Qu J; Cai J; Yang R
Med Phys; 2022 Mar; 49(3):1522-1534. PubMed ID: 35034367
[TBL] [Abstract][Full Text] [Related]
15. Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning.
Rossi M; Belotti G; Paganelli C; Pella A; Barcellini A; Cerveri P; Baroni G
Med Phys; 2021 Nov; 48(11):7112-7126. PubMed ID: 34636429
[TBL] [Abstract][Full Text] [Related]
16. Patient-specific deep learning model to enhance 4D-CBCT image for radiomics analysis.
Zhang Z; Huang M; Jiang Z; Chang Y; Lu K; Yin FF; Tran P; Wu D; Beltran C; Ren L
Phys Med Biol; 2022 Apr; 67(8):. PubMed ID: 35313293
[No Abstract] [Full Text] [Related]
17. Feasibility of CycleGAN enhanced low dose CBCT imaging for prostate radiotherapy dose calculation.
Chan Y; Li M; Parodi K; Belka C; Landry G; Kurz C
Phys Med Biol; 2023 May; 68(10):. PubMed ID: 37054740
[TBL] [Abstract][Full Text] [Related]
18. Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography.
Harms J; Lei Y; Wang T; Zhang R; Zhou J; Tang X; Curran WJ; Liu T; Yang X
Med Phys; 2019 Sep; 46(9):3998-4009. PubMed ID: 31206709
[TBL] [Abstract][Full Text] [Related]
19. Validation of a deformable image registration technique for cone beam CT-based dose verification.
Moteabbed M; Sharp GC; Wang Y; Trofimov A; Efstathiou JA; Lu HM
Med Phys; 2015 Jan; 42(1):196-205. PubMed ID: 25563260
[TBL] [Abstract][Full Text] [Related]
20. Synthetic CT generation from CBCT images via deep learning.
Chen L; Liang X; Shen C; Jiang S; Wang J
Med Phys; 2020 Mar; 47(3):1115-1125. PubMed ID: 31853974
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
