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For: Sourbron SP, Buckley DL. Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol. 2012;57:R1-33. [PMID: 22173205 DOI: 10.1088/0031-9155/57/2/r1] [Cited by in Crossref: 236] [Cited by in F6Publishing: 92] [Article Influence: 21.5] [Reference Citation Analysis]
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7 Debus C, Floca R, Ingrisch M, Kompan I, Maier-Hein K, Abdollahi A, Nolden M. MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRI. BMC Bioinformatics 2019;20:31. [PMID: 30651067 DOI: 10.1186/s12859-018-2588-1] [Cited by in Crossref: 12] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
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10 Ertl-Wagner B, Ingrisch M, Niyazi M, Schnell O, Jansen N, Förster S, la Fougère C. [PET-MR in patients with glioblastoma multiforme]. Radiologe 2013;53:682-90. [PMID: 23949437 DOI: 10.1007/s00117-013-2500-y] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]
11 Flouri D, Lesnic D, Chrysochou C, Parikh J, Thelwall P, Sheerin N, Kalra PA, Buckley DL, Sourbron SP. Motion correction of free-breathing magnetic resonance renography using model-driven registration. MAGMA 2021. [PMID: 34160718 DOI: 10.1007/s10334-021-00936-x] [Reference Citation Analysis]
12 Rathore RK, Gupta RK. Dynamic contrast-enhanced MR: importance of reaching the washout phase. Author reply. AJNR Am J Neuroradiol 2013;34:E60. [PMID: 23819160 DOI: 10.3174/ajnr.a3578] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.2] [Reference Citation Analysis]
13 Ingrisch M, Sourbron S. Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer. J Pharmacokinet Pharmacodyn. 2013;40:281-300. [PMID: 23563847 DOI: 10.1007/s10928-013-9315-3] [Cited by in Crossref: 74] [Cited by in F6Publishing: 66] [Article Influence: 8.2] [Reference Citation Analysis]
14 Bliesener Y, Lingala SG, Haldar JP, Nayak KS. Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects. Magn Reson Med 2020;83:1625-39. [PMID: 31605556 DOI: 10.1002/mrm.28024] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
15 Knight SP, Browne JE, Meaney JF, Smith DS, Fagan AJ. A novel anthropomorphic flow phantom for the quantitative evaluation of prostate DCE-MRI acquisition techniques. Phys Med Biol 2016;61:7466-83. [PMID: 27694709 DOI: 10.1088/0031-9155/61/20/7466] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
16 Biglands JD, Radjenovic A, Ridgway JP. Cardiovascular magnetic resonance physics for clinicians: Part II. J Cardiovasc Magn Reson. 2012;14:66. [PMID: 22995744 DOI: 10.1186/1532-429x-14-66] [Cited by in Crossref: 51] [Cited by in F6Publishing: 20] [Article Influence: 5.1] [Reference Citation Analysis]
17 Papanastasiou G, Williams MC, Dweck MR, Alam S, Cooper A, Mirsadraee S, Newby DE, Semple SI. Quantitative assessment of myocardial blood flow in coronary artery disease by cardiovascular magnetic resonance: comparison of Fermi and distributed parameter modeling against invasive methods. J Cardiovasc Magn Reson 2016;18:57. [PMID: 27624746 DOI: 10.1186/s12968-016-0270-1] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 2.3] [Reference Citation Analysis]
18 Hindel S, Söhner A, Maaß M, Sauerwein W, Möllmann D, Baba HA, Kramer M, Lüdemann L. Validation of Blood Volume Fraction Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Porcine Skeletal Muscle. PLoS One 2017;12:e0170841. [PMID: 28141810 DOI: 10.1371/journal.pone.0170841] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 3.0] [Reference Citation Analysis]
19 Bae J, Huang Z, Knoll F, Geras K, Pandit Sood T, Feng L, Heacock L, Moy L, Kim SG. Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach. Magn Reson Med 2022. [PMID: 35001423 DOI: 10.1002/mrm.29148] [Reference Citation Analysis]
20 Iltis I, Choi J, Vollmers M, Shenoi M, Bischof J, Metzger GJ. In vivo detection of the effects of preconditioning on LNCaP tumors by a TNF-α nanoparticle construct using MRI. NMR Biomed 2014;27:1063-9. [PMID: 24980267 DOI: 10.1002/nbm.3157] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 0.8] [Reference Citation Analysis]
21 Papanastasiou G, Williams MC, Dweck MR, Mirsadraee S, Weir N, Fletcher A, Lucatelli C, Patel D, van Beek EJR, Newby DE, Semple SIK. Multimodality quantitative assessments of myocardial perfusion using dynamic contrast enhanced magnetic resonance and 15O-labelled water positron emission tomography imaging. IEEE Trans Radiat Plasma Med Sci 2018;2:259-71. [PMID: 30003181 DOI: 10.1109/TRPMS.2018.2796626] [Cited by in Crossref: 10] [Cited by in F6Publishing: 1] [Article Influence: 2.5] [Reference Citation Analysis]
22 Lehnert J, Kolbitsch C, Wübbeler G, Chiribiri A, Schaeffter T, Elster C. Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging. SIAM J Imaging Sci 2019;12:2035-62. [DOI: 10.1137/19m1246274] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
23 Eilaghi A, Yeung T, d'Esterre C, Bauman G, Yartsev S, Easaw J, Fainardi E, Lee TY, Frayne R. Quantitative Perfusion and Permeability Biomarkers in Brain Cancer from Tomographic CT and MR Images. Biomark Cancer 2016;8:47-59. [PMID: 27398030 DOI: 10.4137/BIC.S31801] [Cited by in Crossref: 3] [Article Influence: 0.5] [Reference Citation Analysis]
24 Tietze A, Nielsen A, Klærke Mikkelsen I, Bo Hansen M, Obel A, Østergaard L, Mouridsen K. Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps. PLoS One 2018;13:e0202906. [PMID: 30256797 DOI: 10.1371/journal.pone.0202906] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
25 Sanz-Requena R, Revert-Ventura A, Martí-Bonmatí L, Alberich-Bayarri A, García-Martí G. Quantitative MR perfusion parameters related to survival time in high-grade gliomas. Eur Radiol 2013;23:3456-65. [PMID: 23839170 DOI: 10.1007/s00330-013-2967-y] [Cited by in Crossref: 14] [Cited by in F6Publishing: 11] [Article Influence: 1.6] [Reference Citation Analysis]
26 Baboli M, Zhang J, Kim SG. Advances in Diffusion and Perfusion MRI for Quantitative Cancer Imaging. Curr Pathobiol Rep 2019;7:129-41. [PMID: 33344067 DOI: 10.1007/s40139-019-00204-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Betrouni N, Tartare G. ProstateAtlas SimDCE: A simulation tool for dynamic contrast enhanced imaging of prostate. IRBM 2015;36:166-9. [DOI: 10.1016/j.irbm.2015.01.015] [Cited by in Crossref: 2] [Article Influence: 0.3] [Reference Citation Analysis]
28 Lobo MR, Green SC, Schabel MC, Gillespie GY, Woltjer RL, Pike MM. Quinacrine synergistically enhances the antivascular and antitumor efficacy of cediranib in intracranial mouse glioma. Neuro Oncol 2013;15:1673-83. [PMID: 24092859 DOI: 10.1093/neuonc/not119] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 1.7] [Reference Citation Analysis]
29 van Herten RL, Chiribiri A, Breeuwer M, Veta M, Scannell CM. Physics-informed neural networks for myocardial perfusion MRI quantification. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102399] [Reference Citation Analysis]
30 Daviller C, Boutelier T, Giri S, Ratiney H, Jolly MP, Vallée JP, Croisille P, Viallon M. Direct Comparison of Bayesian and Fermi Deconvolution Approaches for Myocardial Blood Flow Quantification: In silico and Clinical Validations. Front Physiol 2021;12:483714. [PMID: 33912066 DOI: 10.3389/fphys.2021.483714] [Reference Citation Analysis]
31 Delbary F, Garbarino S. Compartmental analysis of dynamic nuclear medicine data: regularization procedure and application to physiology. Inverse Problems in Science and Engineering 2019;27:1279-97. [DOI: 10.1080/17415977.2018.1512603] [Cited by in Crossref: 2] [Article Influence: 0.5] [Reference Citation Analysis]
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33 Cyran CC, Kazmierczak PM, Hirner H, Moser M, Ingrisch M, Havla L, Michels A, Eschbach R, Schwarz B, Reiser MF, Bruns CJ, Nikolaou K. Regorafenib effects on human colon carcinoma xenografts monitored by dynamic contrast-enhanced computed tomography with immunohistochemical validation. PLoS One 2013;8:e76009. [PMID: 24098755 DOI: 10.1371/journal.pone.0076009] [Cited by in Crossref: 19] [Cited by in F6Publishing: 21] [Article Influence: 2.1] [Reference Citation Analysis]
34 Macdonald JA, Corrado PA, Nguyen SM, Johnson KM, Francois CJ, Magness RR, Shah DM, Golos TG, Wieben O. Uteroplacental and Fetal 4D Flow MRI in the Pregnant Rhesus Macaque. J Magn Reson Imaging 2019;49:534-45. [PMID: 30102431 DOI: 10.1002/jmri.26206] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
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38 Fasler DA, Ingrisch M, Nanz D, Weckbach S, Kyburz D, Fischer DR, Guggenberger R, Andreisek G. Rheumatoid cervical pannus: feasibility of volume and perfusion quantification using dynamic contrast enhanced time resolved MRI. Acta Radiol 2020;61:227-35. [PMID: 31169411 DOI: 10.1177/0284185119854200] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
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54 Eschbach RS, Clevert DA, Hirner-Eppeneder H, Ingrisch M, Moser M, Schuster J, Tadros D, Schneider M, Kazmierczak PM, Reiser M, Cyran CC. Contrast-Enhanced Ultrasound with VEGFR2-Targeted Microbubbles for Monitoring Regorafenib Therapy Effects in Experimental Colorectal Adenocarcinomas in Rats with DCE-MRI and Immunohistochemical Validation. PLoS One 2017;12:e0169323. [PMID: 28060884 DOI: 10.1371/journal.pone.0169323] [Cited by in Crossref: 14] [Cited by in F6Publishing: 18] [Article Influence: 2.8] [Reference Citation Analysis]
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56 Ulas C, Das D, Thrippleton MJ, Valdés Hernández MDC, Armitage PA, Makin SD, Wardlaw JM, Menze BH. Convolutional Neural Networks for Direct Inference of Pharmacokinetic Parameters: Application to Stroke Dynamic Contrast-Enhanced MRI. Front Neurol 2018;9:1147. [PMID: 30671015 DOI: 10.3389/fneur.2018.01147] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 3.7] [Reference Citation Analysis]
57 Elkin R, Nadeem S, LoCastro E, Paudyal R, Hatzoglou V, Lee NY, Shukla-Dave A, Deasy JO, Tannenbaum A. Optimal mass transport kinetic modeling for head and neck DCE-MRI: Initial analysis. Magn Reson Med 2019;82:2314-25. [PMID: 31273818 DOI: 10.1002/mrm.27897] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
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60 Zhao M, Guo LL, Huang N, Wu Q, Zhou L, Zhao H, Zhang J, Fu K. Quantitative analysis of permeability for glioma grading using dynamic contrast-enhanced magnetic resonance imaging. Oncol Lett 2017;14:5418-26. [PMID: 29113174 DOI: 10.3892/ol.2017.6895] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 1.6] [Reference Citation Analysis]
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