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For: Woodruff HC, Shieh C, Hegi-johnson F, Keall PJ, Kipritidis J. Quantifying the reproducibility of lung ventilation images between 4-Dimensional Cone Beam CT and 4-Dimensional CT. Med Phys 2017;44:1771-81. [DOI: 10.1002/mp.12199] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 1.3] [Reference Citation Analysis]
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1 Yang Z, Lafata KJ, Chen X, Bowsher J, Chang Y, Wang C, Yin FF. Quantification of lung function on CT images based on pulmonary radiomic filtering. Med Phys 2022;49:7278-86. [PMID: 35770964 DOI: 10.1002/mp.15837] [Reference Citation Analysis]
2 Liu Z, Tian Y, Miao J, Men K, Wang W, Wang X, Zhang T, Bi N, Dai J. Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model. Front Oncol 2022;12:889266. [PMID: 35586492 DOI: 10.3389/fonc.2022.889266] [Reference Citation Analysis]
3 Chang Y, Jiang Z, Segars WP, Zhang Z, Lafata K, Cai J, Yin FF, Ren L. A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms. Phys Med Biol 2021;66. [PMID: 34061044 DOI: 10.1088/1361-6560/ac01b4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
4 Kadoya N, Nemoto H, Kajikawa T, Nakajima Y, Kanai T, Ieko Y, Ikeda R, Sato K, Dobashi S, Takeda K, Jingu K. Evaluation of four-dimensional cone beam computed tomography ventilation images acquired with two different linear accelerators at various gantry speeds using a deformable lung phantom. Phys Med 2020;77:75-83. [PMID: 32795891 DOI: 10.1016/j.ejmp.2020.07.030] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
5 Castillo E, Castillo R, Vinogradskiy Y, Dougherty M, Solis D, Myziuk N, Thompson A, Guerra R, Nair G, Guerrero T. Robust CT ventilation from the integral formulation of the Jacobian. Med Phys 2019;46:2115-25. [PMID: 30779353 DOI: 10.1002/mp.13453] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 3.8] [Reference Citation Analysis]
6 Kipritidis J, Tahir BA, Cazoulat G, Hofman MS, Siva S, Callahan J, Hardcastle N, Yamamoto T, Christensen GE, Reinhardt JM, Kadoya N, Patton TJ, Gerard SE, Duarte I, Archibald-Heeren B, Byrne M, Sims R, Ramsay S, Booth JT, Eslick E, Hegi-Johnson F, Woodruff HC, Ireland RH, Wild JM, Cai J, Bayouth JE, Brock K, Keall PJ. The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging. Med Phys 2019;46:1198-217. [PMID: 30575051 DOI: 10.1002/mp.13346] [Cited by in Crossref: 38] [Cited by in F6Publishing: 38] [Article Influence: 9.5] [Reference Citation Analysis]
7 Bucknell NW, Hardcastle N, Bressel M, Hofman MS, Kron T, Ball D, Siva S. Functional lung imaging in radiation therapy for lung cancer: A systematic review and meta-analysis. Radiother Oncol 2018;129:196-208. [PMID: 30082143 DOI: 10.1016/j.radonc.2018.07.014] [Cited by in Crossref: 38] [Cited by in F6Publishing: 39] [Article Influence: 7.6] [Reference Citation Analysis]
8 Jensen KR, Brink C, Hansen O, Bernchou U. Ventilation measured on clinical 4D-CBCT: Increased ventilation accuracy through improved image quality. Radiotherapy and Oncology 2017;125:459-63. [DOI: 10.1016/j.radonc.2017.10.024] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]