The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Assessment of blood–brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review
Open accessAbstract
There is increasing recognition of the importance of blood-brain barrier (BBB) disruption in aging, dementia, stroke and multiple sclerosis in addition to more commonly-studied pathologies such as tumors. Dynamic contrast-enhanced MRI (DCE-MRI) is a method for studying BBB disruption in vivo. We review pathologies studied, scanning protocols and data analysis procedures to determine the range of available methods and their suitability to different pathologies. We systematically review the existing literature up to February 2014, seeking studies that assessed BBB integrity using T1-weighted DCE-MRI techniques in animals and humans in normal or abnormal brain tissues. The literature search provided 70 studies that were eligible for inclusion, involving 417 animals and 1564 human subjects in total. The pathologies most studied are intracranial neoplasms and acute ischemic strokes. There are large variations in the type of DCE-MRI sequence, the imaging protocols and the contrast agents used. Moreover, studies use a variety of different methods for data analysis, mainly based on model-free measurements and on the Patlak and Tofts models. Consequently, estimated K (Trans) values varied widely. In conclusion, DCE-MRI is shown to provide valuable information in a large variety of applications, ranging from common applications, such as grading of primary brain tumors, to more recent applications, such as assessment of subtle BBB dysfunction in Alzheimer's disease. Further research is required in order to establish consensus-based recommendations for data acquisition and analysis and, hence, improve inter-study comparability and promote wider use of DCE-MRI.
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Cites
GRADE: an emerging consensus on rating quality of evidence and strength of recommendations
Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008) · BMJ · doi:10.1136/bmj.39489.470347.ad · Cited by 23,305
Tofts, P. S., Brix, G., Buckley, D. L., Evelhoch, J. L., Henderson, E., Knopp, M. V., Larsson, H. B. W., Lee, T.-Y., Mayr, N. A., Parker, G. J. M., et al. (1999) · Journal of Magnetic Resonance Imaging · doi:10.1002/(sici)1522-2586(199909)10:3<223::aid-jmri2>3.0.co;2-s · Cited by 3,143
Development, maintenance and disruption of the blood-brain barrier
Obermeier, B., Daneman, R., & Ransohoff, R. M. (2013) · Nature Medicine · doi:10.1038/nm.3407 · Cited by 2,452
Tofts, P. S., & Kermode, A. G. (1991) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910170208 · Cited by 1,456
Parker, G. J. M., Roberts, C., Macdonald, A., Buonaccorsi, G. A., Cheung, S., Buckley, D. L., Jackson, A., Watson, Y., Davies, K., & Jayson, G. C. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21066 · Cited by 660
Pharmacokinetic Parameters in CNS Gd-DTPA Enhanced MR Imaging
Brix, G., Semmler, W., Port, R., Schad, L. R., Layer, G., & Lorenz, W. J. (1991) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199107000-00018 · Cited by 659
Pharmacokinetics of GdDTPA/dimeglumine after intravenous injection into healthy volunteers.
Weinmann, H., Laniado, M., & Mutzel, W. (1984) · PubMed · Cited by 462
Neurological Diseases in Relation to the Blood–Brain Barrier
Rosenberg, G. A. (2012) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2011.197 · Cited by 445
Starr, J. M. (2003) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.74.1.70 · Cited by 434
Classic models for dynamic contrast‐enhanced MRI
Sourbron, S. P., & Buckley, D. L. (2013) · NMR in Biomedicine · doi:10.1002/nbm.2940 · Cited by 434
Cited by
Daulatzai, M. A. (2017) · Journal of Neuroscience Research · doi:10.1002/jnr.23777 · Cited by 429
Blood–brain barrier leakage is increased in parkinson’s disease
Al-Bachari, S., Naish, J. H., Parker, G. J. M., Emsley, H. C. A., & Parkes, L. M. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.593026 · Cited by 263
Thrippleton, M. J., Backes, W. H., Sourbron, S., Ingrisch, M., van Osch, M. J. P., Dichgans, M., Fazekas, F., Ropele, S., Frayne, R., van Oostenbrugge, R. J., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2019.01.013 · Cited by 252
Self-citationTracer kinetic modelling for DCE-MRI quantification of subtle blood–brain barrier permeability
Heye, A. K., Thrippleton, M. J., Armitage, P. A., Valdés Hernández, M. D. C., Makin, S. D., Glatz, A., Sakka, E., & Wardlaw, J. M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.018 · Cited by 187
Self-citationDCE-MRI blood–brain barrier assessment in acute ischemic stroke
Villringer, K., Sanz Cuesta, B. E., Ostwaldt, A.-C., Grittner, U., Brunecker, P., Khalil, A. A., Schindler, K., Eisenblätter, O., Audebert, H., & Fiebach, J. B. (2017) · Neurology · doi:10.1212/wnl.0000000000003566 · Cited by 119
Metadata
- Authors
- Heye, A. K., Culling, R. D., Valdés Hernández, M. D. C., Thrippleton, M. J., & Wardlaw, J. M.
- Venue
- NeuroImage: Clinical
- Publisher
- Elsevier BV
- Published
- 2014
- Volume
- 6
- Pages
- 262-274
- ISSN
- 2213-1582 (ISSN-L)
- OpenAlex
- W2168127946
- Cited by
- 406
- References
- 82
- FWCI
- 1.99
- Type
- journal article
- Language
- en
- Open access
- gold
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- Heye, A. K., Culling, R. D., Valdés Hernández, M. D. C., Thrippleton, M. J., & Wardlaw, J. M. (2014). Assessment of blood–brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review. NeuroImage: Clinical. https://doi.org/10.1016/j.nicl.2014.09.002
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