369 related articles for article (PubMed ID: 31853974)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. 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]
11. A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration.
Uh J; Wang C; Jordan JA; Pirlepesov F; Becksfort JB; Ates O; Krasin MJ; Hua CH
Phys Med Biol; 2023 Jul; 68(16):. PubMed ID: 37442128
[No Abstract] [Full Text] [Related]
12. 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]
13. 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]
14. Dosimetric assessment of patient dose calculation on a deep learning-based synthesized computed tomography image for adaptive radiotherapy.
Lemus OMD; Wang YF; Li F; Jambawalikar S; Horowitz DP; Xu Y; Wuu CS
J Appl Clin Med Phys; 2022 Jul; 23(7):e13595. PubMed ID: 35332646
[TBL] [Abstract][Full Text] [Related]
15. New technique and application of truncated CBCT processing in adaptive radiotherapy for breast cancer.
Xie K; Gao L; Xi Q; Zhang H; Zhang S; Zhang F; Sun J; Lin T; Sui J; Ni X
Comput Methods Programs Biomed; 2023 Apr; 231():107393. PubMed ID: 36739623
[TBL] [Abstract][Full Text] [Related]
16. Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy.
Yuan N; Dyer B; Rao S; Chen Q; Benedict S; Shang L; Kang Y; Qi J; Rong Y
Phys Med Biol; 2020 Jan; 65(3):035003. PubMed ID: 31842014
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Deep learning framework to improve the quality of cone-beam computed tomography for radiotherapy scenarios.
Yang B; Liu Y; Zhu J; Dai J; Men K
Med Phys; 2023 Dec; 50(12):7641-7653. PubMed ID: 37345371
[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. 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]
[Next] [New Search]