170 related articles for article (PubMed ID: 38336759)
1. A 4D-CBCT correction network based on contrastive learning for dose calculation in lung cancer.
Cao N; Wang Z; Ding J; Zhang H; Zhang S; Gao L; Sun J; Xie K; Ni X
Radiat Oncol; 2024 Feb; 19(1):20. PubMed ID: 38336759
[TBL] [Abstract][Full Text] [Related]
2. A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images.
Usui K; Ogawa K; Goto M; Sakano Y; Kyougoku S; Daida H
Radiat Oncol; 2022 Apr; 17(1):69. PubMed ID: 35392947
[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. 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]
5. Anthropomorphic lung phantom based validation of in-room proton therapy 4D-CBCT image correction for dose calculation.
Bondesson D; Meijers A; Janssens G; Rit S; Rabe M; Kamp F; Niepel K; Otter LAD; Both S; Brousmiche S; Dinkel J; Belka C; Parodi K; Knopf A; Kurz C; Landry G
Z Med Phys; 2022 Feb; 32(1):74-84. PubMed ID: 33248812
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy.
Pang B; Si H; Liu M; Fu W; Zeng Y; Liu H; Cao T; Chang Y; Quan H; Yang Z
Med Phys; 2023 Nov; 50(11):6920-6930. PubMed ID: 37800874
[TBL] [Abstract][Full Text] [Related]
9. CBCT-Based synthetic CT image generation using conditional denoising diffusion probabilistic model.
Peng J; Qiu RLJ; Wynne JF; Chang CW; Pan S; Wang T; Roper J; Liu T; Patel PR; Yu DS; Yang X
Med Phys; 2024 Mar; 51(3):1847-1859. PubMed ID: 37646491
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy.
Xue X; Ding Y; Shi J; Hao X; Li X; Li D; Wu Y; An H; Jiang M; Wei W; Wang X
Technol Cancer Res Treat; 2021; 20():15330338211062415. PubMed ID: 34851204
[No Abstract] [Full Text] [Related]
13. Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks.
Amirian M; Montoya-Zegarra JA; Herzig I; Eggenberger Hotz P; Lichtensteiger L; Morf M; Züst A; Paysan P; Peterlik I; Scheib S; Füchslin RM; Stadelmann T; Schilling FP
Med Phys; 2023 Oct; 50(10):6228-6242. PubMed ID: 36995003
[TBL] [Abstract][Full Text] [Related]
14. Cone-beam CT-derived relative stopping power map generation via deep learning for proton radiotherapy.
Harms J; Lei Y; Wang T; McDonald M; Ghavidel B; Stokes W; Curran WJ; Zhou J; Liu T; Yang X
Med Phys; 2020 Sep; 47(9):4416-4427. PubMed ID: 32579710
[TBL] [Abstract][Full Text] [Related]
15. Dosimetric comparison of deformable image registration and synthetic CT generation based on CBCT images for organs at risk in cervical cancer radiotherapy.
Chang Y; Liang Y; Yang B; Qiu J; Pei X; Xu XG
Radiat Oncol; 2023 Jan; 18(1):3. PubMed ID: 36604687
[TBL] [Abstract][Full Text] [Related]
16. Dosimetric evaluation of 4D-CBCT reconstructed by Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for carbon ion therapy of lung cancer.
Shrestha D; Tsai MY; Qin N; Zhang Y; Jia X; Wang J
Med Phys; 2019 Sep; 46(9):4087-4094. PubMed ID: 31299097
[TBL] [Abstract][Full Text] [Related]
17. Improving cone-beam CT quality using a cycle-residual connection with a dilated convolution-consistent generative adversarial network.
Deng L; Zhang M; Wang J; Huang S; Yang X
Phys Med Biol; 2022 Jul; 67(14):. PubMed ID: 35728794
[No Abstract] [Full Text] [Related]
18. A clinical 3D/4D CBCT-based treatment dose monitoring system.
Qin A; Gersten D; Liang J; Liu Q; Grill I; Guerrero T; Stevens C; Yan D
J Appl Clin Med Phys; 2018 Nov; 19(6):166-176. PubMed ID: 30306710
[TBL] [Abstract][Full Text] [Related]
19. Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction.
Zhang Z; Liu J; Yang D; Kamilov US; Hugo GD
Med Phys; 2023 Feb; 50(2):808-820. PubMed ID: 36412165
[TBL] [Abstract][Full Text] [Related]
20. SPARE: Sparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan.
Shieh CC; Gonzalez Y; Li B; Jia X; Rit S; Mory C; Riblett M; Hugo G; Zhang Y; Jiang Z; Liu X; Ren L; Keall P
Med Phys; 2019 Sep; 46(9):3799-3811. PubMed ID: 31247134
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
