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For: Valladares A, Beyer T, Rausch I. Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: An overview of existing designs. Med Phys 2020;47:2023-37. [PMID: 31981214 DOI: 10.1002/mp.14045] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Valladares A, Oberoi G, Berg A, Beyer T, Unger E, Rausch I. Additively manufactured, solid object structures for adjustable image contrast in Magnetic Resonance Imaging. Z Med Phys 2022:S0939-3889(22)00037-X. [PMID: 35597743 DOI: 10.1016/j.zemedi.2022.03.003] [Reference Citation Analysis]
2 Lennie E, Tsoumpas C, Sourbron S. Multimodal phantoms for clinical PET/MRI. EJNMMI Phys 2021;8:62. [PMID: 34436671 DOI: 10.1186/s40658-021-00408-0] [Reference Citation Analysis]
3 Zhang L, Ren Z, Xu C, Li Q, Chen J. Influencing Factors and Prognostic Value of 18F-FDG PET/CT Metabolic and Volumetric Parameters in Non-Small Cell Lung Cancer. Int J Gen Med 2021;14:3699-706. [PMID: 34321915 DOI: 10.2147/IJGM.S320744] [Reference Citation Analysis]
4 Zhao B. Understanding Sources of Variation to Improve the Reproducibility of Radiomics. Front Oncol 2021;11:633176. [PMID: 33854969 DOI: 10.3389/fonc.2021.633176] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
5 Saint Martin MJ, Orlhac F, Akl P, Khalid F, Nioche C, Buvat I, Malhaire C, Frouin F. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. MAGMA 2021;34:355-66. [PMID: 33180226 DOI: 10.1007/s10334-020-00892-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Rinaldi L, Pezzotta F, Santaniello T, De Marco P, Bianchini L, Origgi D, Cremonesi M, Milani P, Mariani M, Botta F. HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images. Phys Med 2022;97:13-24. [PMID: 35334407 DOI: 10.1016/j.ejmp.2022.03.010] [Reference Citation Analysis]
7 Cao X, Lee K, Huang Q. Bayesian variable selection in logistic regression with application to whole-brain functional connectivity analysis for Parkinson's disease. Stat Methods Med Res 2021;30:826-42. [PMID: 33308007 DOI: 10.1177/0962280220978990] [Reference Citation Analysis]
8 Greffier J, Dabli D, Hamard A, Belaouni A, Akessoul P, Frandon J, Beregi JP. Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study. Quant Imaging Med Surg 2022;12:229-43. [PMID: 34993074 DOI: 10.21037/qims-21-215] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection.Insights Imaging. 2020;11:91. [PMID: 32785796 DOI: 10.1186/s13244-020-00887-2] [Cited by in Crossref: 41] [Cited by in F6Publishing: 41] [Article Influence: 20.5] [Reference Citation Analysis]
10 Gallivanone F, D'Ambrosio D, Carne I, D'Arcangelo M, Montagna P, Giroletti E, Poggi P, Vellani C, Moro L, Castiglioni I. A tri-modal tissue-equivalent anthropomorphic phantom for PET, CT and multi-parametric MRI radiomics. Phys Med 2022;98:28-39. [PMID: 35489129 DOI: 10.1016/j.ejmp.2022.04.007] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Ericsson-Szecsenyi R, Zhang G, Redler G, Feygelman V, Rosenberg S, Latifi K, Ceberg C, Moros EG. Robustness Assessment of Images From a 0.35T Scanner of an Integrated MRI-Linac: Characterization of Radiomics Features in Phantom and Patient Data. Technol Cancer Res Treat 2022;21:15330338221099113. [PMID: 35521966 DOI: 10.1177/15330338221099113] [Reference Citation Analysis]
12 Läppchen T, Meier LP, Fürstner M, Prenosil GA, Krause T, Rominger A, Klaeser B, Hentschel M. 3D printing of radioactive phantoms for nuclear medicine imaging. EJNMMI Phys 2020;7:22. [PMID: 32323035 DOI: 10.1186/s40658-020-00292-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
13 Strzelecki M, Piórkowski A, Obuchowicz R. Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging. JCM 2022;11:2526. [DOI: 10.3390/jcm11092526] [Reference Citation Analysis]
14 Bauer DF, Adlung A, Brumer I, Golla AK, Russ T, Oelschlegel E, Tollens F, Clausen S, Aumüller P, Schad LR, Nörenberg D, Zöllner FG. An anthropomorphic pelvis phantom for MR-guided prostate interventions. Magn Reson Med 2021. [PMID: 34652819 DOI: 10.1002/mrm.29043] [Reference Citation Analysis]
15 Cao X, Wang X, Xue C, Zhang S, Huang Q, Liu W. A Radiomics Approach to Predicting Parkinson's Disease by Incorporating Whole-Brain Functional Activity and Gray Matter Structure. Front Neurosci 2020;14:751. [PMID: 32760248 DOI: 10.3389/fnins.2020.00751] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
16 Rausch I, Valladares A, Sundar LKS, Beyer T, Hacker M, Meyerspeer M, Unger E. Standard MRI-based attenuation correction for PET/MRI phantoms: a novel concept using MRI-visible polymer. EJNMMI Phys 2021;8:18. [PMID: 33599876 DOI: 10.1186/s40658-021-00364-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Crasto N, Kirubarajan A, Sussman D. Anthropomorphic brain phantoms for use in MRI systems: a systematic review. MAGMA 2021. [PMID: 34463866 DOI: 10.1007/s10334-021-00953-w] [Reference Citation Analysis]