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For: Masi M, Landoni V, Faiella A, Farneti A, Marzi S, Guerrisi M, Sanguineti G. Comparison of rigid and deformable coregistration between mpMRI and CT images in radiotherapy of prostate bed cancer recurrence. Phys Med 2021;92:32-9. [PMID: 34847400 DOI: 10.1016/j.ejmp.2021.11.010] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Xu Y, Williamson JF, Dogan N, Harry T, Ford JC. A novel edge gradient distance metric for automated evaluation of deformable image registration quality. Physica Medica 2022;103:26-36. [DOI: 10.1016/j.ejmp.2022.09.010] [Reference Citation Analysis]
2 Dossun C, Niederst C, Noel G, Meyer P. Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies. Phys Med 2022;101:137-57. [PMID: 36007403 DOI: 10.1016/j.ejmp.2022.08.011] [Reference Citation Analysis]
3 Huo H, Shen S, He D, Liu B, Yang F. Head-to-head comparison of 68Ga-PSMA-11 PET/CT and 68Ga-PSMA-11 PET/MRI in the detection of biochemical recurrence of prostate cancer: summary of head-to-head comparison studies. Prostate Cancer Prostatic Dis 2022. [PMID: 35931759 DOI: 10.1038/s41391-022-00581-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Bottero M, Faiella A, Giannarelli D, Farneti A, D'urso P, Bertini L, Landoni V, Vici P, Sanguineti G. A prospective study assessing the pattern of response of local disease at DCE-MRI after salvage radiotherapy for prostate cancer. Clinical and Translational Radiation Oncology 2022;35:21-6. [DOI: 10.1016/j.ctro.2022.04.010] [Reference Citation Analysis]
5 Yang B, Chen X, Li J, Zhu J, Men K, Dai J. A feasible method to evaluate deformable image registration with deep learning–based segmentation. Physica Medica 2022;95:50-6. [DOI: 10.1016/j.ejmp.2022.01.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]