Cited by 117Advanced MRI Techniques and ApplicationsBarrier Structure and Function StudiesAdvanced Neuroimaging Techniques and Applications

Accurate Determination of Blood–Brain Barrier Permeability Using Dynamic Contrast-Enhanced T1-Weighted MRI: A Simulation and in vivo Study on Healthy Subjects and Multiple Sclerosis Patients

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Cramer, Stig P, Larsson, Henrik BW·2014·Journal of Cerebral Blood Flow & Metabolism

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE–MRI) is increasingly used to estimate permeability in situations with subtle blood–brain barrier (BBB) leakage. However, the method's ability to differentiate such low values from zero is unknown, and no consensus exists on optimal selection of total measurement duration, temporal resolution, and modeling approach under varying physiologic circumstances. To estimate accuracy and precision of the DCE–MRI method we generated simulated data using a two-compartment model and progressively down-sampled and truncated the data to mimic low temporal resolution and short total measurement duration. Model fit was performed with the Patlak, the extended Tofts, and the Tikhonov two-compartment (Tik-2CM) models. Overall, 17 healthy controls were scanned to obtain in vivo data. Long total measurement duration (15 minutes) and high temporal resolution (1.25 seconds) greatly improved accuracy and precision for all three models, enabling us to differentiate values of permeability as low as 0.1 ml/100 g/min from zero. The Patlak model yielded highest accuracy and precision for permeability values <0.3 ml/100 g/min, but for higher values the Tik-2CM performed best. Our results emphasize the importance of optimal parameter setup and model selection when characterizing low BBB permeability.

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  • In multiple sclerosis patients, increased blood-brain barrier permeability has been shown by DCE-MRI in the cortex and adjacent white matter, corpus callosum and internal capsule, and thalamic gray matter.

    “More recently, an increase in BBB permeability has been shown in MS patients by DCE-MRI in the cortex and adjacent white matter (392), corpus callosum and internal capsule (121-123, 421, 574), as well as thalamic gray matter (123).”

    From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [121]

    The Results (page not supplied) report DCE-MRI permeability estimates in contrast-enhancing MS lesions, supporting the general point that BBB permeability was assessed in MS patients. The Materials and methods (page not supplied) describe lesion locations only as periventricular, juxtacortical, or infratentorial; the paper does not report increased permeability specifically in the corpus callosum, internal capsule, or thalamic gray matter. The cortex and adjacent white matter are attached to a different citation in the citing sentence. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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  1. Larsson HB, Stubgaard M, Frederiksen JL, Jensen M, Henriksen O, Paulson OB. Quantitation of blood-brain barrier defect by magnetic resonance imaging and gadolinium-DTPA in patients with multiple sclerosis and brain tumors. Magn Reson Med. 1990;16:117–131. doi: 10.1002/mrm.1910160111. [DOI] [PubMed] [Google Scholar] · DOI
  2. Tofts PS, Kermode AG. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med. 1991;17:357–367. doi: 10.1002/mrm.1910170208. [DOI] [PubMed] [Google Scholar] · DOI
  3. Tofts PSP, Brix GG, Buckley DLD, Evelhoch JLJ, Henderson EE, Knopp MVM, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10:223–232. doi: 10.1002/(sici)1522-2586(199909)10:3<223::aid-jmri2>3.0.co;2-s. [DOI] [PubMed] [Google Scholar] · DOI
  4. Sourbron SP, Buckley DL. On the scope and interpretation of the Tofts models for DCE-MRI. Magn Reson Med. 2011;66:735–745. doi: 10.1002/mrm.22861. [DOI] [PubMed] [Google Scholar] · DOI
  5. Luypaert R, Sourbron S, de Mey J. Validity of perfusion parameters obtained using the modified Tofts model: a simulation study. Magn Reson Med. 2010;65:1491–1497. doi: 10.1002/mrm.22728. [DOI] [PubMed] [Google Scholar] · DOI
  6. Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983;3:1–7. doi: 10.1038/jcbfm.1983.1. [DOI] [PubMed] [Google Scholar] · DOI
  7. Larsson HBW, Courivaud F, Rostrup E, Hansen AE. Measurement of brain perfusion, blood volume, and blood-brain barrier permeability, using dynamic contrast-enhanced T1-weighted MRI at 3 tesla. Magn Reson Med. 2009;62:1270–1281. doi: 10.1002/mrm.22136. [DOI] [PubMed] [Google Scholar] · DOI
  8. Larsson HB, Rosenbaum S, Fritz-Hansen T. Quantification of the effect of water exchange in dynamic contrast MRI perfusion measurements in the brain and heart. Magn Reson Med. 2001;46:272–281. doi: 10.1002/mrm.1188. [DOI] [PubMed] [Google Scholar] · DOI
  9. Moody AR, Martel A, Kenton A, Allder S, Horsfield MA, Delay G, et al. Contrast-reduced imaging of tissue concentration and arterial level (CRITICAL) for assessment of cerebral hemodynamics in acute stroke by magnetic resonance. Invest Radiol. 2000;35:401–411. doi: 10.1097/00004424-200007000-00002. [DOI] [PubMed] [Google Scholar] · DOI
  10. Singh A, Haris M, Rathore D, Purwar A, Sarma M, Bayu G, et al. Quantification of physiological and hemodynamic indices using T(1) dynamic contrast-enhanced MRI in intracranial mass lesions. J Magn Reson Imaging. 2007;26:871–880. doi: 10.1002/jmri.21080. [DOI] [PubMed] [Google Scholar] · DOI
  11. Larsson HBW, Hansen AE, Berg HK, Rostrup E, Haraldseth O. Dynamic contrast-enhanced quantitative perfusion measurement of the brain using T1-weighted MRI at 3T. J Magn Reson Imaging. 2008;27:754–762. doi: 10.1002/jmri.21328. [DOI] [PubMed] [Google Scholar] · DOI
  12. Sourbron S, Ingrisch M, Siefert A, Reiser M, Herrmann K. Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI. Magn Reson Med. 2009;62:205–217. doi: 10.1002/mrm.22005. [DOI] [PubMed] [Google Scholar] · DOI
  13. Hansen AE, Pedersen H, Rostrup E, Larsson HBW. Partial volume effect (PVE) on the arterial input function (AIF) in T1-weighted perfusion imaging and limitations of the multiplicative rescaling approach. Magn Reson Med. 2009;62:1055–1059. doi: 10.1002/mrm.22098. [DOI] [PubMed] [Google Scholar] · DOI
  14. Jelescu IO, Leppert IR, Narayanan S, Araújo D, Arnold DL, Pike GB. Dual-temporal resolution dynamic contrast-enhanced MRI protocol for blood-brain barrier permeability measurement in enhancing multiple sclerosis lesions. J Magn Reson Imaging. 2011;33:1291–1300. doi: 10.1002/jmri.22565. [DOI] [PubMed] [Google Scholar] · DOI
  15. Brix G, Kiessling F, Lucht R, Darai S, Wasser K, Delorme S, et al. Microcirculation and microvasculature in breast tumors: pharmacokinetic analysis of dynamic MR image series. Magn Reson Med. 2004;52:420–429. doi: 10.1002/mrm.20161. [DOI] [PubMed] [Google Scholar] · DOI
  16. Lund H, Krakauer M, Skimminge A, Sellebjerg F, Garde E, Siebner HR, et al. Blood-brain barrier permeability of normal appearing white matter in relapsing-remitting multiple sclerosis. PLoS ONE. 2013;8:e56375. doi: 10.1371/journal.pone.0056375. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI
  17. Silver NC, Tofts PS, Symms MR, Barker GJ, Thompson AJ, Miller DH. Quantitative contrast-enhanced magnetic resonance imaging to evaluate blood-brain barrier integrity in multiple sclerosis: a preliminary study. Mult Scler. 2001;7:75–82. doi: 10.1177/135245850100700201. [DOI] [PubMed] [Google Scholar] · DOI
  18. Kim SY, Buckwalter M, Soreq H, Vezzani A, Kaufer D. Blood-brain barrier dysfunction-induced inflammatory signaling in brain pathology and epileptogenesis. Epilepsia. 2012;53:37–44. doi: 10.1111/j.1528-1167.2012.03701.x. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI · Paper page
  19. Oby E, Janigro D. The blood-brain barrier and epilepsy. Epilepsia. 2006;47:1761–1774. doi: 10.1111/j.1528-1167.2006.00817.x. [DOI] [PubMed] [Google Scholar] · DOI
  20. Starr JM, Farrall AJ, Armitage P, McGurn B, Wardlaw J. Blood-brain barrier permeability in Alzheimer's disease: a case-control MRI study. Psychiatry Res. 2009;171:232–241. doi: 10.1016/j.pscychresns.2008.04.003. [DOI] [PubMed] [Google Scholar] · DOI
  21. Anderson VC, Lenar DP, Quinn JF, Rooney WD. The blood-brain barrier and microvascular water exchange in alzheimer's disease. Cardiovasc Psychiatry Neurol. 2011;2011:1–9. doi: 10.1155/2011/615829. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI
  22. Taheri S, Gasparovic C, Huisa BN, Adair JC, Edmonds E, Prestopnik J, et al. Blood-brain barrier permeability abnormalities in vascular cognitive impairment. Stroke. 2011;42:2158–2163. doi: 10.1161/STROKEAHA.110.611731. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI · Paper page
  23. Edvinsson L, Tfelt-Hansen P. The blood-brain barrier in migraine treatment. Cephalalgia. 2008;28:1245–1258. doi: 10.1111/j.1468-2982.2008.01675.x. [DOI] [PubMed] [Google Scholar] · DOI
  24. Gao H-M, Le Li, Zhang K-L, Chen X-H, Tian S-Q, Zhang Z-L. Impact of migraine attacks on the blood-brain barrier. Chin Med J (Engl) 2010;123:2559–2561. [PubMed] [Google Scholar]
  25. Winkler MKL, Chassidim Y, Lublinsky S, Revankar GS, Major S, Kang E-J, et al. Impaired neurovascular coupling to ictal epileptic activity and spreading depolarization in a patient with subarachnoid hemorrhage: possible link to blood-brain barrier dysfunction. Epilepsia. 2012;53:22–30. doi: 10.1111/j.1528-1167.2012.03699.x. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI
  26. Bell RD, Winkler EA, Sagare AP, Singh I, LaRue B, Deane R, et al. Pericytes control key neurovascular functions and neuronal phenotype in the adult brain and during brain aging. Neuron. 2010;68:409–427. doi: 10.1016/j.neuron.2010.09.043. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI · Paper page
  27. Iannotti F, Fieschi C, Alfano B, Picozzi P, Mansi L, Pozzilli C, et al. Simplified, noninvasive PET measurement of blood-brain barrier permeability. J Comput Assist Tomogr. 1987;11:390–397. doi: 10.1097/00004728-198705000-00004. [DOI] [PubMed] [Google Scholar] · DOI
  28. Ingrisch M, Sourbron S, Morhard D, Ertl-Wagner B, Kümpfel T, Hohlfeld R, et al. Quantification of perfusion and permeability in multiple sclerosis. Invest Radiol. 2012;47:252–258. doi: 10.1097/RLI.0b013e31823bfc97. [DOI] [PubMed] [Google Scholar] · DOI
  29. Taheri S, Rosenberg GA, Ford C. Quantification of blood-to-brain transfer rate in multiple sclerosis. Mult Scler Relat Disord. 2013;2:124–132. doi: 10.1016/j.msard.2012.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI
  30. Ewing JR, Knight RA, Nagaraja TN, Yee JS, Nagesh V, Whitton PA, et al. Patlak plots of Gd-DTPA MRI data yield blood-brain transfer constants concordant with those of 14C-sucrose in areas of blood-brain opening. Magn Reson Med. 2003;50:283–292. doi: 10.1002/mrm.10524. [DOI] [PubMed] [Google Scholar] · DOI
  31. Aerts HJWL, Jaspers K, Backes WH. The precision of pharmacokinetic parameters in dynamic contrast-enhanced magnetic resonance imaging: the effect of sampling frequency and duration. Phys Med Biol. 2011;56:5665–5678. doi: 10.1088/0031-9155/56/17/013. [DOI] [PubMed] [Google Scholar] · DOI
  32. Larsson C, Kleppestø M, Rasmussen I, Salo R, Vardal J, Brandal P, et al. Sampling requirements in DCE-MRI based analysis of high grade gliomas: simulations and clinical results. J Magn Reson Imaging. 2013;37:818–829. doi: 10.1002/jmri.23866. [DOI] [PubMed] [Google Scholar] · DOI
  33. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med. 1996;36:715–725. doi: 10.1002/mrm.1910360510. [DOI] [PubMed] [Google Scholar] · DOI

Cited by

Sorted by citations

Authors
Cramer, S. P., & Larsson, H. B.
Affiliations
University of Copenhagen (DK)
Corresponding author
Larsson, Henrik BW
Venue
Journal of Cerebral Blood Flow & Metabolism
Published
2014-07-30
Volume
34
Issue
10
Pages
1655-1665
ISSN
  • 0271-678X (ISSN-L)
  • 1559-7016
OpenAlex
W2083426712
Cited by
117
References
1
Citations by year
2026: 9 · 2025: 9 · 2024: 9 · 2023: 4 · 2022: 6 · 2021: 14 · 2020: 20 · 2019: 4 · 2018: 10 · 2017: 11 · 2016: 12 · 2015: 7 · 2014: 2
FWCI
8.33
Type
journal article
Language
en
Open access
Locations
  • Journal of Cerebral Blood Flow & Metabolism (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Life Sciences › Neuroscience › Neurology › Barrier Structure and Function Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
blood-brain barrier permeability, dynamic contrast-enhanced MRI, T1-weighted imaging, multiple sclerosis, Patlak model, extended Tofts model, two-compartment model, measurement time, temporal resolution, simulation study
MeSH terms
Adult, Algorithms, Blood-Brain Barrier, Computer Simulation, Female, Humans, Magnetic Resonance Imaging, Male, Models, Biological, Multiple Sclerosis, Permeability, Sensitivity and Specificity, Young Adult

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