BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

530 related articles for article (PubMed ID: 36183234)

  • 1. 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]  

  • 2. 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]  

  • 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. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy.
    Liu Y; Lei Y; Wang T; Fu Y; Tang X; Curran WJ; Liu T; Patel P; Yang X
    Med Phys; 2020 Jun; 47(6):2472-2483. PubMed ID: 32141618
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Synthetic CT generation based on CBCT using respath-cycleGAN.
    Deng L; Hu J; Wang J; Huang S; Yang X
    Med Phys; 2022 Aug; 49(8):5317-5329. PubMed ID: 35488299
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 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. 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]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 27.