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Cited by in F6Publishing
For: Bergamino M, Bonzano L, Levrero F, Mancardi GL, Roccatagliata L. A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors. Phys Med. 2014;30:635-643. [PMID: 24793824 DOI: 10.1016/j.ejmp.2014.04.005] [Cited by in Crossref: 40] [Cited by in F6Publishing: 36] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Norbeck O, Sprenger T, Avventi E, Rydén H, Kits A, Berglund J, Skare S. Optimizing 3D EPI for rapid T 1 ‐weighted imaging. Magn Reson Med 2020;84:1441-55. [DOI: 10.1002/mrm.28222] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Hsu YH, Huang Z, Ferl GZ, Ng CM. GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab. PLoS One 2015;10:e0118421. [PMID: 25786263 DOI: 10.1371/journal.pone.0118421] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.4] [Reference Citation Analysis]
4 Spanakis M, Kontopodis E, Van Cauter S, Sakkalis V, Marias K. Assessment of DCE-MRI parameters for brain tumors through implementation of physiologically-based pharmacokinetic model approaches for Gd-DOTA. J Pharmacokinet Pharmacodyn 2016;43:529-47. [PMID: 27647272 DOI: 10.1007/s10928-016-9493-x] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 0.8] [Reference Citation Analysis]
5 Shah NJ, da Silva NA, Yun SD. Perfusion weighted imaging using combined gradient/spin echo EPIK: Brain tumour applications in hybrid MR-PET. Hum Brain Mapp 2021;42:4144-54. [PMID: 30761676 DOI: 10.1002/hbm.24537] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
6 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]
7 Demirhan A. The effect of feature selection on multivariate pattern analysis of structural brain MR images. Physica Medica 2018;47:103-11. [DOI: 10.1016/j.ejmp.2018.03.002] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 1.8] [Reference Citation Analysis]
8 Chen Y, Lu B, Shu Y, Sun Y. Spatiotemporal Dynamics of Cerebral Vascular Permeability in Type 2 Diabetes-Related Cerebral Microangiopathy. Front Endocrinol 2022;12:805637. [DOI: 10.3389/fendo.2021.805637] [Reference Citation Analysis]
9 Kong Z, Yan C, Zhu R, Wang J, Wang Y, Wang Y, Wang R, Feng F, Ma W. Imaging biomarkers guided anti-angiogenic therapy for malignant gliomas. Neuroimage Clin 2018;20:51-60. [PMID: 30069427 DOI: 10.1016/j.nicl.2018.07.001] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 4.0] [Reference Citation Analysis]
10 Khalifa F, Soliman A, El-Baz A, Abou El-Ghar M, El-Diasty T, Gimel’farb G, Ouseph R, Dwyer AC. Models and methods for analyzing DCE-MRI: a review. Med Phys. 2014;41:124301. [PMID: 25471985 DOI: 10.1118/1.4898202] [Cited by in Crossref: 128] [Cited by in F6Publishing: 118] [Article Influence: 18.3] [Reference Citation Analysis]
11 Gonzales RA, Zhang Q, Papież BW, Werys K, Lukaschuk E, Popescu IA, Burrage MK, Shanmuganathan M, Ferreira VM, Piechnik SK. MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks. Front Cardiovasc Med 2021;8:768245. [PMID: 34888366 DOI: 10.3389/fcvm.2021.768245] [Reference Citation Analysis]
12 Xiong H, Yin P, Li X, Yang C, Zhang D, Huang X, Tang Z. The features of cerebral permeability and perfusion detected by dynamic contrast-enhanced magnetic resonance imaging with Patlak model in relapsing-remitting multiple sclerosis. Ther Clin Risk Manag 2019;15:233-40. [PMID: 30787618 DOI: 10.2147/TCRM.S189598] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 2.3] [Reference Citation Analysis]
13 Ryu JK, Rhee SJ, Song JY, Cho SH, Jahng GH. Characteristics of quantitative perfusion parameters on dynamic contrast-enhanced MRI in mammographically occult breast cancer. J Appl Clin Med Phys 2016;17:377-90. [PMID: 27685105 DOI: 10.1120/jacmp.v17i5.6091] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 0.8] [Reference Citation Analysis]
14 Zhang J, Liu H, Tong H, Wang S, Yang Y, Liu G, Zhang W. Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges. Contrast Media Mol Imaging 2017;2017:7064120. [PMID: 29097933 DOI: 10.1155/2017/7064120] [Cited by in Crossref: 32] [Cited by in F6Publishing: 33] [Article Influence: 6.4] [Reference Citation Analysis]
15 Chang E, Pohling C, Natarajan A, Witney TH, Kaur J, Xu L, Gowrishankar G, D'Souza AL, Murty S, Schick S, Chen L, Wu N, Khaw P, Mischel P, Abbasi T, Usmani S, Mallick P, Gambhir SS. AshwaMAX and Withaferin A inhibits gliomas in cellular and murine orthotopic models. J Neurooncol 2016;126:253-64. [PMID: 26650066 DOI: 10.1007/s11060-015-1972-1] [Cited by in Crossref: 22] [Cited by in F6Publishing: 19] [Article Influence: 3.1] [Reference Citation Analysis]
16 Zhao M, van Straten D, Broekman MLD, Préat V, Schiffelers RM. Nanocarrier-based drug combination therapy for glioblastoma. Theranostics 2020;10:1355-72. [PMID: 31938069 DOI: 10.7150/thno.38147] [Cited by in Crossref: 37] [Cited by in F6Publishing: 45] [Article Influence: 18.5] [Reference Citation Analysis]
17 Zhang J, Xue W, Xu K, Yi L, Guo Y, Xie T, Tong H, Zhou B, Wang S, Li Q, Liu H, Chen X, Fang J, Zhang W. Dual inhibition of PFKFB3 and VEGF normalizes tumor vasculature, reduces lactate production, and improves chemotherapy in glioblastoma: insights from protein expression profiling and MRI. Theranostics 2020;10:7245-59. [PMID: 32641990 DOI: 10.7150/thno.44427] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
18 Lin X, Lee M, Buck O, Woo KM, Zhang Z, Hatzoglou V, Omuro A, Arevalo-Perez J, Thomas AA, Huse J, Peck K, Holodny AI, Young RJ. Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma. AJNR Am J Neuroradiol. 2017;38:485-491. [PMID: 27932505 DOI: 10.3174/ajnr.a5023] [Cited by in Crossref: 42] [Cited by in F6Publishing: 18] [Article Influence: 7.0] [Reference Citation Analysis]
19 Wu Y, Yan Y, Gao X, Yang L, Li Y, Guo X, Xie J, Wang K, Sun X. Gd-encapsulated carbonaceous dots for accurate characterization of tumor vessel permeability in magnetic resonance imaging. Nanomedicine: Nanotechnology, Biology and Medicine 2019;21:102074. [DOI: 10.1016/j.nano.2019.102074] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
20 Yin XX, Zhang Y, Cao J, Wu JL, Hadjiloucas S. Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework. Comput Methods Programs Biomed 2016;137:87-114. [PMID: 28110743 DOI: 10.1016/j.cmpb.2016.08.026] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 1.2] [Reference Citation Analysis]
21 Wang F, Sha Y, Zhao M, Wan H, Zhang F, Cheng Y, Tang W. High-Resolution Diffusion-Weighted Imaging Improves the Diagnostic Accuracy of Dynamic Contrast-Enhanced Sinonasal Magnetic Resonance Imaging. J Comput Assist Tomogr 2017;41:199-205. [PMID: 27560026 DOI: 10.1097/RCT.0000000000000502] [Cited by in Crossref: 14] [Cited by in F6Publishing: 4] [Article Influence: 2.8] [Reference Citation Analysis]
22 Veksler R, Shelef I, Friedman A. Blood-brain barrier imaging in human neuropathologies. Arch Med Res 2014;45:646-52. [PMID: 25453223 DOI: 10.1016/j.arcmed.2014.11.016] [Cited by in Crossref: 32] [Cited by in F6Publishing: 31] [Article Influence: 4.0] [Reference Citation Analysis]
23 Yang JF, Zhao ZH, Zhang Y, Zhao L, Yang LM, Zhang MM, Wang BY, Wang T, Lu BC. Dual-input two-compartment pharmacokinetic model of dynamic contrast-enhanced magnetic resonance imaging in hepatocellular carcinoma. World J Gastroenterol 2016; 22(13): 3652-3662 [PMID: 27053857 DOI: 10.3748/wjg.v22.i13.3652] [Cited by in CrossRef: 10] [Cited by in F6Publishing: 12] [Article Influence: 1.7] [Reference Citation Analysis]
24 Wang J, Zhang H, Ni D, Fan W, Qu J, Liu Y, Jin Y, Cui Z, Xu T, Wu Y, Bu W, Yao Z. High-Performance Upconversion Nanoprobes for Multimodal MR Imaging of Acute Ischemic Stroke. Small 2016;12:3591-600. [PMID: 27219071 DOI: 10.1002/smll.201601144] [Cited by in Crossref: 21] [Cited by in F6Publishing: 17] [Article Influence: 3.5] [Reference Citation Analysis]
25 Raja R, Rosenberg GA, Caprihan A. MRI measurements of Blood-Brain Barrier function in dementia: A review of recent studies. Neuropharmacology 2018;134:259-71. [PMID: 29107626 DOI: 10.1016/j.neuropharm.2017.10.034] [Cited by in Crossref: 52] [Cited by in F6Publishing: 48] [Article Influence: 10.4] [Reference Citation Analysis]
26 Wu L, Yang C, Halim A, Rao S, Xu P, Feng W, Chen C, Ji Y, Zhu J, Zeng M. Contrast-enhanced magnetic resonance imaging perfusion can predict microvascular invasion in patients with hepatocellular carcinoma (between 1 and 5 cm). Abdom Radiol (NY) 2022. [PMID: 35113174 DOI: 10.1007/s00261-022-03423-6] [Reference Citation Analysis]
27 Foley D, Browne JE, Zhuang X, Sheane B, O'Driscoll D, Cannon D, Sheehy N, Meaney JF, Fagan AJ. The utility of deformable image registration for small artery visualisation in contrast-enhanced whole body MR angiography. Phys Med 2014;30:898-908. [PMID: 25182374 DOI: 10.1016/j.ejmp.2014.08.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
28 Beuzit L, Eliat PA, Brun V, Ferré JC, Gandon Y, Bannier E, Saint-Jalmes H. Dynamic contrast-enhanced MRI: Study of inter-software accuracy and reproducibility using simulated and clinical data. J Magn Reson Imaging 2016;43:1288-300. [PMID: 26687041 DOI: 10.1002/jmri.25101] [Cited by in Crossref: 21] [Cited by in F6Publishing: 22] [Article Influence: 3.0] [Reference Citation Analysis]
29 Shi J, Yu W, Xu L, Yin N, Liu W, Zhang K, Liu J, Zhang Z. Bioinspired Nanosponge for Salvaging Ischemic Stroke via Free Radical Scavenging and Self-Adapted Oxygen Regulating. Nano Lett 2020;20:780-9. [PMID: 31830790 DOI: 10.1021/acs.nanolett.9b04974] [Cited by in Crossref: 18] [Cited by in F6Publishing: 17] [Article Influence: 6.0] [Reference Citation Analysis]
30 Hatzoglou V, Oh JH, Buck O, Lin X, Lee M, Shukla-Dave A, Young RJ, Peck KK, Vachha B, Holodny AI, Grommes C. Pretreatment dynamic contrast-enhanced MRI biomarkers correlate with progression-free survival in primary central nervous system lymphoma. J Neurooncol 2018;140:351-8. [PMID: 30073640 DOI: 10.1007/s11060-018-2960-z] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
31 Chen Y, Liu S, Wang Y, Kang Y, Haacke EM. STrategically Acquired Gradient Echo (STAGE) imaging, part I: Creating enhanced T1 contrast and standardized susceptibility weighted imaging and quantitative susceptibility mapping. Magn Reson Imaging 2018;46:130-9. [PMID: 29056394 DOI: 10.1016/j.mri.2017.10.005] [Cited by in Crossref: 35] [Cited by in F6Publishing: 31] [Article Influence: 7.0] [Reference Citation Analysis]
32 Lu Y, Peng W, Song J, Chen T, Wang X, Hou Z, Yan Z, Koh TS. On the potential use of dynamic contrast-enhanced (DCE) MRI parameters as radiomic features of cervical cancer. Med Phys 2019;46:5098-109. [PMID: 31523829 DOI: 10.1002/mp.13821] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
33 Lee MD, Young MG, Fatterpekar GM. "The Pituitary within GRASP" - Golden-Angle Radial Sparse Parallel Dynamic MRI Technique and Applications to the Pituitary Gland. Semin Ultrasound CT MR 2021;42:307-15. [PMID: 34147165 DOI: 10.1053/j.sult.2021.04.007] [Reference Citation Analysis]
34 Schimpf O, Hindel S, Lüdemann L. Assessment of micronecrotic tumor tissue using dynamic contrast-enhanced magnetic resonance imaging. Phys Med 2017;34:38-47. [PMID: 28139354 DOI: 10.1016/j.ejmp.2017.01.010] [Cited by in Crossref: 1] [Article Influence: 0.2] [Reference Citation Analysis]
35 Taxt T, Andersen E, Jiřík R. Single voxel vascular transport functions of arteries, capillaries and veins; and the associated arterial input function in dynamic susceptibility contrast magnetic resonance brain perfusion imaging. Magn Reson Imaging 2021;84:101-14. [PMID: 34461158 DOI: 10.1016/j.mri.2021.08.008] [Reference Citation Analysis]
36 Shao C, Shen A, Zhang M, Meng X, Song C, Liu Y, Gao X, Wang P, Bu W. Oxygen Vacancies Enhanced CeO 2 :Gd Nanoparticles for Sensing a Tumor Vascular Microenvironment by Magnetic Resonance Imaging. ACS Nano 2018;12:12629-37. [DOI: 10.1021/acsnano.8b07387] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 3.8] [Reference Citation Analysis]
37 Liu F, Cuenod CA, Thomassin-Naggara I, Chemouny S, Rozenholc Y. Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences. Med Image Anal 2019;51:125-43. [PMID: 30419490 DOI: 10.1016/j.media.2018.10.007] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
38 Garpebring A, Löfstedt T. Parameter estimation using weighted total least squares in the two-compartment exchange model. Magn Reson Med 2018;79:561-7. [PMID: 28349618 DOI: 10.1002/mrm.26677] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.4] [Reference Citation Analysis]
39 Bergamino M, Barletta L, Castellan L, Mancardi G, Roccatagliata L. Dynamic Contrast-Enhanced MRI in the Study of Brain Tumors. Comparison Between the Extended Tofts-Kety Model and a Phenomenological Universalities (PUN) Algorithm. J Digit Imaging 2015;28:748-54. [PMID: 25776769 DOI: 10.1007/s10278-015-9788-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
40 van Hoof RH, Hermeling E, Truijman MT, van Oostenbrugge RJ, Daemen JW, van der Geest RJ, van Orshoven NP, Schreuder AH, Backes WH, Daemen MJ, Wildberger JE, Kooi ME. Phase-based vascular input function: Improved quantitative DCE-MRI of atherosclerotic plaques. Med Phys 2015;42:4619-28. [PMID: 26233189 DOI: 10.1118/1.4924949] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
41 van Zijl P, Knutsson L. In vivo magnetic resonance imaging and spectroscopy. Technological advances and opportunities for applications continue to abound. J Magn Reson 2019;306:55-65. [PMID: 31377150 DOI: 10.1016/j.jmr.2019.07.034] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
42 Xi Y, Kang X, Wang N, Liu T, Zhu Y, Cheng G, Wang K, Li C, Guo F, Yin H. Differentiation of primary central nervous system lymphoma from high-grade glioma and brain metastasis using arterial spin labeling and dynamic contrast-enhanced magnetic resonance imaging. European Journal of Radiology 2019;112:59-64. [DOI: 10.1016/j.ejrad.2019.01.008] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 3.7] [Reference Citation Analysis]
43 Mohammadian-Behbahani MR, Kamali-Asl AR. Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies. Phys Med 2016;32:1543-50. [PMID: 27876537 DOI: 10.1016/j.ejmp.2016.11.011] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]