Siewerdsen JH, Jaffray DA. Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter. Med Phys. 2001;28(2):220–31.
Article CAS PubMed Google Scholar
Schulze R, Heil U, Groβ D, Bruellmann DD, Dranischnikow E, Schwanecke U, Schoemer E. Artefacts in CBCT: a review. Dentomaxillofac Radiol. 2011;40(5):265–73.
Article CAS PubMed PubMed Central Google Scholar
Kurz C, Dedes G, Resch A, Reiner M, Ganswindt U, Nijhuis R, Thieke C, Belka C, Parodi K, Landry G. Comparing cone-beam CT intensity correction methods for dose recalculation in adaptive intensity-modulated photon and proton therapy for head and neck cancer. Acta Oncol. 2015;54(9):1651–7.
Article CAS PubMed Google Scholar
Elsayad K, Kriz J, Reinartz G, Scobioala S, Ernst I. Cone-beam CT-guided radiotherapy in the management of lung cancer. Strahlenther Onkol. 2016;192(2):83.
Chen S, Le Q, Mutaf Y, Lu W, Nichols EM, Yi BY, Leven T, Prado KL, D’Souza WD. Feasibility of CBCT-based dose with a patient-specific stepwise HU-to-density curve to determine time of replanning. J Appl Clin Med Phys. 2017;18(5):64–9.
Article CAS PubMed PubMed Central Google Scholar
Hatton J, McCurdy B, Greer PB. Cone beam computerized tomography: the effect of calibration of the Hounsfield unit number to electron density on dose calculation accuracy for adaptive radiation therapy. Phys Med Biol. 2009;54(15):N329.
Almatani T, Hugtenburg RP, Lewis RD, Barley SE, Edwards MA. Automated algorithm for CBCT-based dose calculations of prostate radiotherapy with bilateral hip prostheses. Br J Radiol. 2016;89(1066):20160443.
Article PubMed PubMed Central Google Scholar
De Smet M, Schuring D, Nijsten S, Verhaegen F. Accuracy of dose calculations on kV cone beam CT images of lung cancer patients. Med Phys. 2016;43(11):5934–41.
Mainegra-Hing E, Kawrakow I. Variance reduction techniques for fast Monte Carlo CBCT scatter correction calculations. Phys Med Biol. 2010;55(16):4495.
Thing RS, Bernchou U, Mainegra-Hing E, Hansen O, Brink C. Hounsfield unit recovery in clinical cone beam CT images of the thorax acquired for image guided radiation therapy. Phys Med Biol. 2016;61(15):5781.
Article CAS PubMed Google Scholar
Zöllner C, Rit S, Kurz C, Vilches-Freixas G, Kamp F, Dedes G, Belka C, Parodi K, Landry G. Decomposing a prior-CT-based cone-beam CT projection correction algorithm into scatter and beam hardening components. Phys Imag Radiat Oncol. 2017;3:49–52.
Sun M, Star-Lack JM. Improved scatter correction using adaptive scatter kernel superposition. Phys Med Biol. 2010;55(22):6695.
Article CAS PubMed Google Scholar
Niu T, Sun M, Star-Lack J, Gao H, Fan Q, Zhu L. Shading correction for on-board cone-beam CT in radiation therapy using planning MDCT images. Med Phys. 2010;37(10):5395–406.
Niu T, Al-Basheer A, Zhu L. Quantitative cone-beam CT imaging in radiation therapy using planning CT as a prior: first patient studies. Med Phys. 2012;39(4):1991–2000.
Article PubMed PubMed Central Google Scholar
Kurz C, Kamp F, Park YK, Zöllner C, Rit S, Hansen D, Podesta M, Sharp GC, Li M, Reiner M. Investigating deformable image registration and scatter correction for CBCT-based dose calculation in adaptive IMPT. Med Phys. 2016;43(10):5635–46.
Maier J, Sawall S, Knaup M, Kachelrieß M. Deep scatter estimation (DSE): accurate real-time scatter estimation for X-ray CT using a deep convolutional neural network. J Nondestr Eval. 2018;37:1–9.
Kida S, Kaji S, Nawa K, Imae T, Nakamoto T, Ozaki S, Ohta T, Nozawa Y, Nakagawa K. Visual enhancement of cone-beam CT by use of CycleGAN. Med Phys. 2020;47(3):998–1010.
Peng J, Qiu RLJ, Wynne JF, Chang CW, Pan S, Wang T, Roper J, Liu T, Patel PR, Yu DS. CBCT-Based synthetic CT image generation using conditional denoising diffusion probabilistic model. Med Phys. 2024;51(3):1847–59.
Article CAS PubMed Google Scholar
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M. The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045–57.
Article PubMed PubMed Central Google Scholar
A.A. Yorke, G.C. McDonald, D. Solis, T. Guerrero, Pelvic Reference Data, The Cancer Imaging Archive, 2019.
Rossi M, Cerveri P. Comparison of supervised and unsupervised approaches for the generation of synthetic CT from cone-beam CT. Diagnostics. 2021;11(8):1435.
Article PubMed PubMed Central Google Scholar
Zhu Y-M, Cochoff SM, Sukalac R. Automatic patient table removal in CT images. J Digit Imaging. 2012;25(4):480–5.
Article PubMed PubMed Central Google Scholar
Niu T, Zhu L. Overview of x-ray scatter in cone-beam computed tomography and its correction methods. Current Med Imaging. 2010;6(2):82–9.
Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2009;29(1):196–205.
Biguri A, Dosanjh M, Hancock S, Soleimani M. TIGRE: a MATLAB-GPU toolbox for CBCT image reconstruction. Biomed Phys Eng Express. 2016;2(5): 055010.
Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Adv Neural Inf Process Syst. 2020;33:6840–51.
Gao Q, Shan H. CoCoDiff: a contextual conditional diffusion model for low-dose CT image denoising. SPIE; 2022. p. 92–8.
Q. Lyu, G. Wang, Conversion between CT and MRI images using diffusion and score-matching models, arXiv preprint arXiv:2209.12104 (2022).
Pan S, Wang T, Qiu RLJ, Axente M, Chang C-W, Peng J, Patel AB, Shelton J, Patel SA, Roper J. 2D medical image synthesis using transformer-based denoising diffusion probabilistic model. Phys Med Biol. 2023;68(10): 105004.
Article PubMed Central Google Scholar
Kurz C, Maspero M, Savenije MHF, Landry G, Kamp F, Pinto M, Li M, Parodi K, Belka C, Van den Berg CAT. CBCT correction using a cycle-consistent generative adversarial network and unpaired training to enable photon and proton dose calculation. Phys Med Biol. 2019;64(22): 225004.
Article CAS PubMed Google Scholar
Gao L, Xie K, Wu X, Lu Z, Li C, Sun J, Lin T, Sui J, Ni X. Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy. Radiat Oncol. 2021;16:1–16.
Liu J, Yan H, Cheng H, Liu J, Sun P, Wang B, Mao R, Du C, Luo S. CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation. Quant Imaging Med Surg. 2021;11(12):4820.
Article PubMed PubMed Central Google Scholar
Xue X, Ding Y, Shi J, Hao X, Li X, Li D, Wu Y, An H, Jiang M, Wei W. Cone beam CT (CBCT) based synthetic CT generation using deep learning methods for dose calculation of nasopharyngeal carcinoma radiotherapy. Technol Cancer Res Treat. 2021;20:15330338211062416.
Article CAS PubMed PubMed Central Google Scholar
M.J. Cardoso, W. Li, R. Brown, N. Ma, E. Kerfoot, Y. Wang, B. Murrey, A. Myronenko, C. Zhao, D. Yang, Monai: An open-source framework for deep learning in healthcare, arXiv preprint arXiv:2211.02701 (2022).
Low DA, Harms WB, Mutic S, Purdy JA. A technique for the quantitative evaluation of dose distributions. Med Phys. 1998;25(5):656–61.
Article CAS PubMed Google Scholar
Marks LB, Yorke ED, Jackson A, Ten Haken RK, Constine LS, Eisbruch A, Bentzen SM, Nam J, Deasy JO. Use of normal tissue complication probability models in the clinic. Int J Radiat Oncol Biol Phys. 2010;76(3):S10–9.
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