Skip to main content

Human cerebral blood volume measurements using dynamic contrast enhancement in comparison to dynamic susceptibility contrast MRI



Cerebral blood volume (CBV) is an important parameter for the assessment of brain tumors, usually obtained using dynamic susceptibility contrast (DSC) MRI. However, this method often suffers from low spatial resolution and high sensitivity to susceptibility artifacts and usually does not take into account the effect of tissue permeability. The plasma volume (v p) can also be extracted from dynamic contrast enhancement (DCE) MRI. The aim of this study was to investigate whether DCE can be used for the measurement of cerebral blood volume in place of DSC for the assessment of patients with brain tumors.


Twenty-eight subjects (17 healthy subjects and 11 patients with glioblastoma) were scanned using DCE and DSC. v p and CBV values were measured and compared in different brain components in healthy subjects and in the tumor area in patients.


Significant high correlations were detected between v p and CBV in healthy subjects in the different brain components; white matter, gray matter, and arteries, correlating with the known increased tissue vascularity, and within the tumor area in patients.


This work proposes the use of DCE as an alternative method to DSC for the assessment of blood volume, given the advantages of its higher spatial resolution, its lower sensitivity to susceptibility artifacts, and its ability to provide additional information regarding tissue permeability.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3


  1. 1.

    Al-Okaili RN, Krejza J, Wang S, Woo JH, Melhem ER (2006) Advanced MR imaging techniques in the diagnosis of intraaxial brain tumors in adults. Radiographics 26(Suppl 1):S173–S189

    PubMed  Article  Google Scholar 

  2. 2.

    Cha S (2006) Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol 27:475–487

    CAS  PubMed  Google Scholar 

  3. 3.

    Law M (2009) Advanced imaging techniques in brain tumors. Cancer Imaging 9 Spec No A: S4-9

  4. 4.

    Law M, Yang S, Wang H, Babb JS, Johnson G et al (2003) Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 24:1989–1998

    PubMed  Google Scholar 

  5. 5.

    Fatterpekar GM, Galheigo D, Narayana A, Johnson G, Knopp E (2012) Treatment-related change versus tumor recurrence in high-grade gliomas: a diagnostic conundrum—use of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI. AJR Am J Roentgenol 198:19–26

    PubMed  Article  Google Scholar 

  6. 6.

    Ostergaard L, Sorensen AG, Kwong KK, Weisskoff RM, Gyldensted C et al (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: experimental comparison and preliminary results. Magn Reson Med 36:726–736

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 36:715–725

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Sourbron S, Ingrisch M, Siefert A, Reiser M, Herrmann K (2009) Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI. Magn Reson Med 62:205–217

    PubMed  Article  Google Scholar 

  9. 9.

    Sourbron SP, Heilmann M, Biffar A, Walczak C, Vautier J et al (2008) Tracer kinetic analysis of a simultaneous T1- and T2* measurement in a tumor model. The International Society for Magnetic Resonance in Medicine, Toronto Ontario Canada

    Google Scholar 

  10. 10.

    Vonken EP, van Osch MJ, Bakker CJ, Viergever MA (2000) Simultaneous quantitative cerebral perfusion and Gd-DTPA extravasation measurement with dual-echo dynamic susceptibility contrast MRI. Magn Reson Med 43:820–827

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Sourbron SP, Buckley DL (2013) Classic models for dynamic contrast-enhanced MRI. NMR Biomed 26:1004–1027

    PubMed  Article  Google Scholar 

  12. 12.

    Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223–232

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Jain R (2013) Measurements of tumor vascular leakiness using DCE in brain tumors: clinical applications. NMR Biomed 26:1042–1049

    PubMed  Article  Google Scholar 

  14. 14.

    Pope WB, Young JR, Ellingson BM (2011) Advances in MRI assessment of gliomas and response to anti-VEGF therapy. Curr Neurol Neurosci Rep 11:336–344

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  15. 15.

    Leach MO, Brindle KM, Evelhoch JL, Griffiths JR, Horsman MR et al (2005) The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer 92:1599–1610

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  16. 16.

    Tofts PS, Kermode AG (1991) Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med 17:357–367

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Daldrup H, Shames DM, Wendland M, Okuhata Y, Link TM et al (1998) Correlation of dynamic contrast-enhanced MR imaging with histologic tumor grade: comparison of macromolecular and small-molecular contrast media. AJR Am J Roentgenol 171:941–949

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Larsen VA, Simonsen HJ, Law I, Larsson HB, Hansen AE (2013) Evaluation of dynamic contrast-enhanced T1-weighted perfusion MRI in the differentiation of tumor recurrence from radiation necrosis. Neuroradiology 55:361–369

    PubMed  Article  Google Scholar 

  19. 19.

    Liberman G, Louzoun Y, Ben Bashat D (2013) T1 mapping using variable flip angle SPGR data with flip angle correction. Imaging, Journal of Magnetic Resonance

    Google Scholar 

  20. 20.

    Liberman G, Nadav G, Louzoun Y, Artzi M, Ben Bashat D (2014) Bolus arrival time extraction using super temporal resolution analysis of DCE. The International Society for Magnetic Resonance in Medicine, Milan, Italy

    Google Scholar 

  21. 21.

    Deoni SC, Peters TM, Rutt BK (2005) High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn Reson Med 53:237–241

    PubMed  Article  Google Scholar 

  22. 22.

    Deoni SC (2007) High-resolution T1 mapping of the brain at 3T with driven equilibrium single pulse observation of T1 with high-speed incorporation of RF field inhomogeneities (DESPOT1-HIFI). J Magn Reson Imaging 26:1106–1111

    PubMed  Article  Google Scholar 

  23. 23.

    Deoni SC, Rutt BK, Peters TM (2003) Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Magn Reson Med 49:515–526

    PubMed  Article  Google Scholar 

  24. 24.

    Fluckiger JU, Schabel MC, Dibella EV (2009) Model-based blind estimation of kinetic parameters in dynamic contrast enhanced (DCE)-MRI. Magn Reson Med 62:1477–1486

    PubMed Central  PubMed  Article  Google Scholar 

  25. 25.

    Murase K (2004) Efficient method for calculating kinetic parameters using T1-weighted dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Med 51:858–862

    PubMed  Article  Google Scholar 

  26. 26.

    Bagher-Ebadian H, Jain R, Nejad-Davarani SP, Mikkelsen T, Lu M et al (2012) Model selection for DCE-T1 studies in glioblastoma. Magn Reson Med 68:241–251

    PubMed Central  PubMed  Article  Google Scholar 

  27. 27.

    Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 27:859–867

    CAS  PubMed  Google Scholar 

  28. 28.

    Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1):S208–S219

    PubMed  Article  Google Scholar 

  29. 29.

    Artzi M, Aizenstein O, Jonas-Kimchi T, Myers V, Hallevi H et al (2013) FLAIR lesion segmentation: application in patients with brain tumors and acute ischemic stroke. Eur J Radiol 82:1512–1518

    PubMed  Article  Google Scholar 

  30. 30.

    Awasthi R, Pandey CM, Sahoo P, Behari S, Kumar V et al (2012) Dynamic contrast-enhanced magnetic resonance imaging-derived kep as a potential biomarker of matrix metalloproteinase 9 expression in patients with glioblastoma multiforme: a pilot study. J Comput Assist Tomogr 36:125–130

    PubMed  Article  Google Scholar 

  31. 31.

    Nadav G, Liberman G, Artzi M, Kiryati N, Ben Bashat D (2014) Flow and permeability estimation from DCE data: 2-compartment exchange and Tofts models comparison. The International Society for Magnetic Resonance in Medicine, Milan, Italy

    Google Scholar 

Download references


We acknowledge Vicki Myers for editorial assistance. This work was performed in partial fulfillment of the requirements for the PhD degree of Artzi Moran, Sackler Faculty of Medicine, Tel Aviv University, Israel.

Ethical standards and patient consent

We declare that all human studies have been approved by the by the Tel Aviv Sourasky Medical Center Review Board, and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. We declare that all patients gave informed consent prior to inclusion in this study.

Conflict of interest

We declare that we have no conflict of interest.

Author information



Corresponding author

Correspondence to Dafna Ben Bashat.

Additional information

Moran Artzi and Gilad Liberman contributed equally to this study.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Artzi, M., Liberman, G., Nadav, G. et al. Human cerebral blood volume measurements using dynamic contrast enhancement in comparison to dynamic susceptibility contrast MRI. Neuroradiology 57, 671–678 (2015).

Download citation


  • MRI
  • Dynamic contrast enhancement
  • Dynamic susceptibility contrast
  • Cerebral blood volume
  • Brain tumor