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For: Duan C, Kallehauge JF, Bretthorst GL, Tanderup K, Ackerman JJ, Garbow JR. Are complex DCE-MRI models supported by clinical data? Magn Reson Med. 2017;77:1329-1339. [PMID: 26946317 DOI: 10.1002/mrm.26189] [Cited by in Crossref: 25] [Cited by in F6Publishing: 24] [Article Influence: 4.2] [Reference Citation Analysis]
Number Citing Articles
1 Lecler A, Balvay D, Cuenod C, Marais L, Zmuda M, Sadik J, Galatoire O, Farah E, El Methni J, Zuber K, Bergès O, Savatovsky J, Fournier L. Quality‐based pharmacokinetic model selection on DCE‐MRI for characterizing orbital lesions. J Magn Reson Imaging 2019;50:1514-25. [DOI: 10.1002/jmri.26747] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 2.3] [Reference Citation Analysis]
2 Liu S, Yu X, Yang S, Hu P, Hu Y, Chen X, Li Y, Zhang Z, Li C, Lu Q. Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer. Front Oncol 2021;11:610338. [PMID: 33842316 DOI: 10.3389/fonc.2021.610338] [Reference Citation Analysis]
3 Shao J, Zhang Z, Liu H, Song Y, Yan Z, Wang X, Hou Z. DCE-MRI pharmacokinetic parameter maps for cervical carcinoma prediction. Comput Biol Med 2020;118:103634. [PMID: 32174312 DOI: 10.1016/j.compbiomed.2020.103634] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Hansen MB, Tietze A, Haack S, Kallehauge J, Mikkelsen IK, Østergaard L, Mouridsen K. Robust estimation of hemo-dynamic parameters in traditional DCE-MRI models. PLoS One 2019;14:e0209891. [PMID: 30605459 DOI: 10.1371/journal.pone.0209891] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
5 Klawer EME, van Houdt PJ, Simonis FFJ, van den Berg CAT, Pos FJ, Heijmink SWTPJ, Isebaert S, Haustermans K, van der Heide UA. Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients. Magn Reson Med 2019;81:3358-69. [PMID: 30656738 DOI: 10.1002/mrm.27646] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
6 Inglese M, Ordidge KL, Honeyfield L, Barwick TD, Aboagye EO, Waldman AD, Grech-Sollars M. Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models. Neuroradiology 2019;61:1375-86. [PMID: 31392385 DOI: 10.1007/s00234-019-02265-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
7 Brix G, Salehi Ravesh M, Griebel J. Two-compartment modeling of tissue microcirculation revisited. Med Phys 2017;44:1809-22. [DOI: 10.1002/mp.12196] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
8 Spees WM, Sukstanskii AL, Bretthorst GL, Neil JJ, Ackerman JJH. Rat Brain Global Ischemia-Induced Diffusion Changes Revisited: Biophysical Modeling of the Water and NAA MR "Diffusion Signal". Magn Reson Med 2022. [PMID: 35452137 DOI: 10.1002/mrm.29262] [Reference Citation Analysis]
9 Callewaert B, Jones EAV, Himmelreich U, Gsell W. Non-Invasive Evaluation of Cerebral Microvasculature Using Pre-Clinical MRI: Principles, Advantages and Limitations. Diagnostics (Basel) 2021;11:926. [PMID: 34064194 DOI: 10.3390/diagnostics11060926] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Fuentes D, Thompson E, Jacobsen M, Crouch AC, Layman RR, Riviere B, Cressman E. Imaging-based characterization of convective tissue properties. Int J Hyperthermia 2020;37:155-63. [PMID: 33426993 DOI: 10.1080/02656736.2020.1845403] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Ng TSC, Seethamraju RT, Bueno R, Gill RR. Clinical Implementation of a Free-Breathing, Motion-Robust Dynamic Contrast-Enhanced MRI Protocol to Evaluate Pleural Tumors. AJR Am J Roentgenol 2020;215:94-104. [PMID: 32348181 DOI: 10.2214/AJR.19.21612] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Duan C, Kallehauge JF, Pérez-Torres CJ, Bretthorst GL, Beeman SC, Tanderup K, Ackerman JJH, Garbow JR. Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF. Mol Imaging Biol 2018;20:150-9. [PMID: 28536804 DOI: 10.1007/s11307-017-1090-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.4] [Reference Citation Analysis]
13 Morozov D, Quirk JD, Beeman SC. Toward noninvasive quantification of adipose tissue oxygenation with MRI. Int J Obes (Lond) 2020;44:1776-83. [PMID: 32231252 DOI: 10.1038/s41366-020-0567-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
14 Besson FL, Fernandez B, Faure S, Mercier O, Seferian A, Mignard X, Mussot S, le Pechoux C, Caramella C, Botticella A, Levy A, Parent F, Bulifon S, Montani D, Mitilian D, Fadel E, Planchard D, Besse B, Ghigna-Bellinzoni MR, Comtat C, Lebon V, Durand E. 18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data. EJNMMI Res 2020;10:88. [PMID: 32734484 DOI: 10.1186/s13550-020-00671-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Paudyal R, Lu Y, Hatzoglou V, Moreira A, Stambuk HE, Oh JH, Cunanan KM, Aramburu Nunez D, Mazaheri Y, Gonen M, Ho A, Fagin JA, Wong RJ, Shaha A, Tuttle RM, Shukla-Dave A. Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers. NMR Biomed 2020;33:e4166. [PMID: 31680360 DOI: 10.1002/nbm.4166] [Cited by in Crossref: 5] [Cited by in F6Publishing: 9] [Article Influence: 1.7] [Reference Citation Analysis]
16 Georgiou L, Sharma N, Broadbent DA, Wilson DJ, Dall BJ, Gangi A, Buckley DL. Estimating breast tumor blood flow during neoadjuvant chemotherapy using interleaved high temporal and high spatial resolution MRI. Magn Reson Med 2018;79:317-26. [PMID: 28370289 DOI: 10.1002/mrm.26684] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 1.8] [Reference Citation Analysis]
17 Chen S, Shu Z, Li Y, Chen B, Tang L, Mo W, Shao G, Shao F. Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients. Front Oncol 2020;10:1410. [PMID: 32923392 DOI: 10.3389/fonc.2020.01410] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
18 Mittermeier A, Ertl-Wagner B, Ricke J, Dietrich O, Ingrisch M. Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application. Phys Med Biol 2019;64:18NT02. [PMID: 31404913 DOI: 10.1088/1361-6560/ab3a5a] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
19 Zöllner FG, Dastrù W, Irrera P, Longo DL, Bennett KM, Beeman SC, Bretthorst GL, Garbow JR. Analysis Protocol for Dynamic Contrast Enhanced (DCE) MRI of Renal Perfusion and Filtration. Methods Mol Biol 2021;2216:637-53. [PMID: 33476028 DOI: 10.1007/978-1-0716-0978-1_38] [Reference Citation Analysis]
20 Nagaraja TN, Elmghirbi R, Brown SL, Schultz LR, Lee IY, Keenan KA, Panda S, Cabral G, Mikkelsen T, Ewing JR. Reproducibility and relative stability in magnetic resonance imaging indices of tumor vascular physiology over a period of 24h in a rat 9L gliosarcoma model. Magn Reson Imaging 2017;44:131-9. [PMID: 28887206 DOI: 10.1016/j.mri.2017.09.003] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.6] [Reference Citation Analysis]
21 Pedersen M, Irrera P, Dastrù W, Zöllner FG, Bennett KM, Beeman SC, Bretthorst GL, Garbow JR, Longo DL. Dynamic Contrast Enhancement (DCE) MRI-Derived Renal Perfusion and Filtration: Basic Concepts. Methods Mol Biol 2021;2216:205-27. [PMID: 33476002 DOI: 10.1007/978-1-0716-0978-1_12] [Reference Citation Analysis]
22 Nijkamp J, Kallehauge J. Editorial for "Convolutional neural network for accelerating the computation of the extended Tofts model in dynamic contrast-enhanced magnetic resonance imaging". J Magn Reson Imaging 2021;53:1911-2. [PMID: 33559246 DOI: 10.1002/jmri.27545] [Reference Citation Analysis]
23 Klawer EME, van Houdt PJ, Pos FJ, Heijmink SWTPJ, van Osch MJP, van der Heide UA. Impact of contrast agent injection duration on dynamic contrast-enhanced MRI quantification in prostate cancer. NMR Biomed 2018;31:e3946. [PMID: 29974981 DOI: 10.1002/nbm.3946] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
24 Yin T, Liu Y, Peeters R, Feng Y, Ni Y. Pancreatic imaging: Current status of clinical practices and small animal studies. World J Methodol 2017; 7(3): 101-107 [PMID: 29026690 DOI: 10.5662/wjm.v7.i3.101] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 1] [Article Influence: 0.6] [Reference Citation Analysis]
25 Liu F, Zhao M, Lu S, Kang L. Study on the Value of DCE-MRI in Differentiating Glossitis and Tongue Cancer and the Intratumour Heterogeneity. Cancer Manag Res 2021;13:6925-34. [PMID: 34526818 DOI: 10.2147/CMAR.S315418] [Reference Citation Analysis]
26 Julie L, Ikram D, Mailyn PL, Augustin L, Afef B, Joevin S, Bentoumi I, Cuenod CA, Daniel B. A free time point model for dynamic contrast enhanced exploration. Magn Reson Imaging 2021;80:39-49. [PMID: 33905829 DOI: 10.1016/j.mri.2021.04.005] [Reference Citation Analysis]