BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

129 related articles for article (PubMed ID: 38525293)

  • 1. Task-based transferable deep-learning scatter correction in cone beam computed tomography: a simulation study.
    Cruz-Bastida JP; Moncada F; Martínez-Dávalos A; Rodríguez-Villafuerte M
    J Med Imaging (Bellingham); 2024 Mar; 11(2):024006. PubMed ID: 38525293
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network.
    Nomura Y; Xu Q; Shirato H; Shimizu S; Xing L
    Med Phys; 2019 Jul; 46(7):3142-3155. PubMed ID: 31077390
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy.
    Lalonde A; Winey B; Verburg J; Paganetti H; Sharp GC
    Phys Med Biol; 2020 Dec; 65(24):. PubMed ID: 32580174
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deep learning for x-ray scatter correction in dedicated breast CT.
    Pautasso JJ; Caballo M; Mikerov M; Boone JM; Michielsen K; Sechopoulos I
    Med Phys; 2023 Apr; 50(4):2022-2036. PubMed ID: 36565012
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications.
    Pinto MC; Mauter F; Michielsen K; Biniazan R; Kappler S; Sechopoulos I
    Med Phys; 2023 Aug; 50(8):4744-4757. PubMed ID: 37394837
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep-learning convolutional neural network-based scatter correction for contrast enhanced digital breast tomosynthesis in both cranio-caudal and mediolateral-oblique views.
    Duan X; Sahu P; Huang H; Zhao W
    J Med Imaging (Bellingham); 2023 Feb; 10(Suppl 2):S22404. PubMed ID: 36937988
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder.
    van der Heyden B; Uray M; Fonseca GP; Huber P; Us D; Messner I; Law A; Parii A; Reisz N; Rinaldi I; Vilches Freixas G; Deutschmann H; Verhaegen F; Steininger P
    Phys Med Biol; 2020 Jul; 65(14):145002. PubMed ID: 32294626
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.
    Zhang X; Jiang Y; Luo C; Li D; Niu T; Yu G
    Med Phys; 2023 Aug; 50(8):5002-5019. PubMed ID: 36734321
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning.
    Jiang Z; Chen Y; Zhang Y; Ge Y; Yin FF; Ren L
    IEEE Trans Med Imaging; 2019 Nov; 38(11):2705-2715. PubMed ID: 31021791
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A generalized image quality improvement strategy of cone-beam CT using multiple spectral CT labels in Pix2pix GAN.
    Jiang Y; Zhang Y; Luo C; Yang P; Wang J; Liang X; Zhao W; Li R; Niu T
    Phys Med Biol; 2022 May; 67(11):. PubMed ID: 35487206
    [No Abstract]   [Full Text] [Related]  

  • 11. A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images.
    Mylonas A; Keall PJ; Booth JT; Shieh CC; Eade T; Poulsen PR; Nguyen DT
    Med Phys; 2019 May; 46(5):2286-2297. PubMed ID: 30929254
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Combining scatter reduction and correction to improve image quality in cone-beam computed tomography (CBCT).
    Jin JY; Ren L; Liu Q; Kim J; Wen N; Guan H; Movsas B; Chetty IJ
    Med Phys; 2010 Nov; 37(11):5634-44. PubMed ID: 21158275
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A convolutional neural network for estimating cone-beam CT intensity deviations from virtual CT projections.
    Rusanov B; Ebert MA; Mukwada G; Hassan GM; Sabet M
    Phys Med Biol; 2021 Oct; 66(21):. PubMed ID: 34534979
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Acuros CTS: A fast, linear Boltzmann transport equation solver for computed tomography scatter - Part II: System modeling, scatter correction, and optimization.
    Wang A; Maslowski A; Messmer P; Lehmann M; Strzelecki A; Yu E; Paysan P; Brehm M; Munro P; Star-Lack J; Seghers D
    Med Phys; 2018 May; 45(5):1914-1925. PubMed ID: 29509973
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Planning CT-guided robust and fast cone-beam CT scatter correction using a local filtration technique.
    Cui H; Jiang X; Fang C; Zhu L; Yang Y
    Med Phys; 2021 Nov; 48(11):6832-6843. PubMed ID: 34662433
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Patient-specific scatter correction in clinical cone beam computed tomography imaging made possible by the combination of Monte Carlo simulations and a ray tracing algorithm.
    Thing RS; Bernchou U; Mainegra-Hing E; Brink C
    Acta Oncol; 2013 Oct; 52(7):1477-83. PubMed ID: 23879648
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A correlated sampling-based Monte Carlo simulation for fast CBCT iterative scatter correction.
    Qin P; Lin G; Li X; Piao Z; Huang S; Wu W; Qi M; Ma J; Zhou L; Xu Y
    Med Phys; 2023 Mar; 50(3):1466-1480. PubMed ID: 36323626
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Local filtration based scatter correction for cone-beam CT using primary modulation.
    Zhu L
    Med Phys; 2016 Nov; 43(11):6199. PubMed ID: 27806607
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.