Jaffray DA, Siewerdsen JH, Wong JW, Martinez AA. Flat-panel cone-beam computed tomography for image-guided radiation therapy. Int J Radiat Oncol Biol Phys. 2002;53(5):1337–49. https://doi.org/10.1016/s0360-3016(02)02884-5.
Siewerdsen JH, Daly MJ, Bakhtiar B, et al. A simple, direct method for x-ray scatter estimation and correction in digital radiography and cone-beam CT. Med Phys. 2006;33(1):187–97. https://doi.org/10.1118/1.2148916.
Article CAS PubMed Google Scholar
Gu R, Dogandžić A (2013) Sparse X-ray CT image reconstruction and blind beam hardening correction via mass attenuation discretization. 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) 244–47. https://doi.org/10.1109/CAMSAP.2013.6714053
Branco D, Mayadev J, Moore K, Ray X. Dosimetric and feasibility evaluation of a CBCT-based daily adaptive radiotherapy protocol for locally advanced cervical cancer. J Appl Clin Med Phys. 2023;24(1): e13783. https://doi.org/10.1002/acm2.13783.
Moazzezi M, Rose B, Kisling K, Moore KL, Ray X. Prospects for daily online adaptive radiotherapy via ethos for prostate cancer patients without nodal involvement using unedited CBCT auto-segmentation. J Appl Clin Med Phys. 2021;22(10):82–93. https://doi.org/10.1002/acm2.13399.
Article PubMed PubMed Central Google Scholar
Bojechko C, Hua P, Sumner W, Guram K, Atwood T, Sharabi A. Adaptive replanning using cone beam CT for deformation of original CT simulation. J Med Radiat Sci. 2022;69(2):267–72. https://doi.org/10.1002/jmrs.549.
Ritschl L, Fahrig R, Knaup M, Maier J, Kachelrieß M. Robust primary modulation-based scatter estimation for cone-beam CT. Med Phys. 2015;42(1):469–78. https://doi.org/10.1118/1.4903261.
Article PubMed PubMed Central Google Scholar
Schafer S, Stayman JW, Zbijewski W, Schmidgunst C, Kleinszig G, Siewerdsen JH. Antiscatter grids in mobile C-arm cone-beam CT: effect on image quality and dose. Med Phys. 2012;39(1):153–9. https://doi.org/10.1118/1.3666947.
Article CAS PubMed Google Scholar
Zhu L, Xie Y, Wang J, Xing L. Scatter correction for cone-beam CT in radiation therapy. Med Phys. 2009;36(6):2258-68. https://doi.org/10.1118/1.3130047
Ning R, Tang X, Conover D. X-ray scatter correction algorithm for cone beam CT imaging. Med Phys. 2004;31(5):1195–202. https://doi.org/10.1118/1.1711475.
Sun M, Star-Lack JM. Improved scatter correction using adaptive scatter kernel superposition. Phys Med Biol. 2010;55(22):6695–720. https://doi.org/10.1088/0031-9155/55/22/007.
Article CAS PubMed Google Scholar
Trapp P, Maier J, Susenburger M, Sawall S, Kachelrieß M. Empirical scatter correction: CBCT scatter artifact reduction without prior information. Med Phys. 2022;49(7):4566–84. https://doi.org/10.1002/mp.15656.
Hsieh J, Molthen RC, Dawson CA, Johnson RH. An iterative approach to the beam hardening correction in cone beam CT. Med Phys. 2000;27(1):23–9. https://doi.org/10.1118/1.598853.
Article CAS PubMed Google Scholar
Stankovic U, Ploeger LS, van Herk M, Sonke JJ. Optimal combination of anti-scatter grids and software correction for CBCT imaging. Med Phys. 2017;44(9):4437–51. https://doi.org/10.1002/mp.12385.
Nomura Y, Xu Q, Shirato H, Shimizu S, Xing L. Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network. Med Phys. 2019;46(7):3142–55. https://doi.org/10.1002/mp.13583.
Article PubMed PubMed Central Google Scholar
Lee H, Lee J. A Deep Learning-Based Scatter Correction of Simulated X-ray Images. Electronics. 2019;8(9):944. https://doi.org/10.3390/electronics8090944.
Hansen DC, Landry G, Kamp F, et al. ScatterNet: A convolutional neural network for cone-beam CT intensity correction. Med Phys. 2018;45(11):4916–26. https://doi.org/10.1002/mp.13175.
Article CAS PubMed Google Scholar
Zhang X, Jiang Y, Luo C, Li D, Niu T, Yu G. Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network. Med Phys. 2023;50(8):5002–19. https://doi.org/10.1002/mp.16277.
Harms J, Lei Y, Wang T, et al. Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography. Med Phys. 2019;46(9):3998–4009. https://doi.org/10.1002/mp.13656.
Article PubMed PubMed Central Google Scholar
Liu Y, Chen X, Zhu J, et al. A two-step method to improve image quality of CBCT with phantom-based supervised and patient-based unsupervised learning strategies. Phys Med Biol. 2022;67(8): 084001. https://doi.org/10.1088/1361-6560/ac6289.
Tang H, Lin YB, Jiang SD, Li Y, Li T, Bao XD. A new dental CBCT metal artifact reduction method based on a dual-domain processing framework. Phys Med Biol. 2023;68(17): 175016. https://doi.org/10.1088/1361-6560/acec29.
Bayaraa T, Hyun CM, Jang TJ, Lee SM, Seo JK. A two-stage approach for beam hardening artifact reduction in low-dose dental CBCT. IEEE Access. 2020;8:225981–94. https://doi.org/10.1109/ACCESS.2020.3044981.
Sisniega A, Zbijewski W, Badal A, et al. Monte Carlo study of the effects of system geometry and antiscatter grids on cone-beam CT scatter distributions. Med Phys. 2013;40(5): 051915. https://doi.org/10.1118/1.4801895.
Article CAS PubMed PubMed Central Google Scholar
Lazos D, Williamson JF. Monte Carlo evaluation of scatter mitigation strategies in cone-beam CT. Med Phys. 2010;37(10):5456–70. https://doi.org/10.1118/1.3488978.
Article PubMed PubMed Central Google Scholar
Poludniowski G, Evans PM, Hansen VN, Webb S. An efficient Monte Carlo-based algorithm for scatter correction in keV cone-beam CT. Phys Med Biol. 2009;54(12):3847–64. https://doi.org/10.1088/0031-9155/54/12/016.
Article CAS PubMed Google Scholar
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. U-Net++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging. 2020;39(6):1856–67. https://doi.org/10.1109/TMI.2019.2959609.
Dong G, Zhang C, Liang X, et al. A deep unsupervised learning model for artifact correction of pelvis cone-beam CT. Front Oncol. 2021;11: 686875. https://doi.org/10.3389/fonc.2021.686875.
Article PubMed PubMed Central Google Scholar
Tong T, Li M. Abdominal or pelvic enhanced CT images within 10 days before surgery of 230 patients with stage II colorectal cancer (StageII-Colorectal-CT). Cancer Imaging Arch. 2022. https://doi.org/10.7937/p5k5-tg43.
Thompson RF, Kanwar A, Merz B, et al. Stress-testing pelvic autosegmentation algorithms using anatomical edge cases (prostate anatomical edge cases). Cancer Imaging Arch. 2023. https://doi.org/10.7937/QSTF-ST65.
Hudobivnik N, Schwarz F, Johnson T, et al. Comparison of proton therapy treatment planning for head tumors with a pencil beam algorithm on dual and single energy CT images. Med Phys. 2016;43(1):495. https://doi.org/10.1118/1.4939106.
White DR, Woodard HQ, Hammond SM. Average soft-tissue and bone models for use in radiation dosimetry. Br J Radiol. 1987;60(717):907–13. https://doi.org/10.1259/0007-1285-60-717-907.
Article CAS PubMed Google Scholar
Berger MJ, Hubbell JH, Seltzer SM, et al. (1987) XCOM: Photon Cross Sections Database. NIST Standard Reference Database 8 (XGAM) https://doi.org/10.18434/T48G6X
Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. Internatioal Conf Med Imag Comput Computerassisted Intervention(MICCAI) 234–41. https://doi.org/10.48550/arXiv.1505.04597
He K, Zhang X, Ren S, Sun J. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. IEEE International Conference on Computer Vision (ICCV). 2015:1026–34. https://doi.org/10.48550/arXiv.1502.01852
Hirayama H, Namito Y, Bielajew AF, Wilderman SJ, Nelson WR. The EGS5 Code System. High Energy Accelerator Research Organization (KEK). 2005.
Garduno E, Herman GT. Superiorization of the ML-EM algorithm. IEEE Trans Nucl Sci. 2013;61:162–72. https://doi.org/10.1109/TNS.2013.2283529.
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