European Radiology

, Volume 18, Issue 1, pp 143–151 | Cite as

Delineation and segmentation of cerebral tumors by mapping blood-brain barrier disruption with dynamic contrast-enhanced CT and tracer kinetics modeling–a feasibility study

  • S. Bisdas
  • X. Yang
  • C. C. T. Lim
  • T. J. Vogl
  • T. S. Koh


Dynamic contrast-enhanced (DCE) imaging is a promising approach for in vivo assessment of tissue microcirculation. Twenty patients with clinical and routine computed tomography (CT) evidence of intracerebral neoplasm were examined with DCE-CT imaging. Using a distributed-parameter model for tracer kinetics modeling of DCE-CT data, voxel-level maps of cerebral blood flow (F), intravascular blood volume (v i) and intravascular mean transit time (t 1) were generated. Permeability-surface area product (PS), extravascular extracellular blood volume (v e) and extraction ratio (E) maps were also calculated to reveal pathologic locations of tracer extravasation, which are indicative of disruptions in the blood-brain barrier (BBB). All maps were visually assessed for quality of tumor delineation and measurement of tumor extent by two radiologists. Kappa (κ) coefficients and their 95% confidence intervals (CI) were calculated to determine the interobserver agreement for each DCE-CT map. There was a substantial agreement for the tumor delineation quality in the F, v e and t 1 maps. The agreement for the quality of the tumor delineation was excellent for the v i, PS and E maps. Concerning the measurement of tumor extent, excellent and nearly excellent agreement was achieved only for E and PS maps, respectively. According to these results, we performed a segmentation of the cerebral tumors on the base of the E maps. The interobserver agreement for the tumor extent quantification based on manual segmentation of tumor in the E maps vs. the computer-assisted segmentation was excellent (κ = 0.96, CI: 0.93–0.99). The interobserver agreement for the tumor extent quantification based on computer segmentation in the mean images and the E maps was substantial (κ = 0.52, CI: 0.42–0.59). This study illustrates the diagnostic usefulness of parametric maps associated with BBB disruption on a physiology-based approach and highlights the feasibility for automatic segmentation of cerebral tumors.


Dynamic contrast-enhanced CT Cerebral tumor Tracer kinetic analysis Blood brain barrier Permeability 


  1. 1.
    Kloska SP, Fischer T, Nabavi DG, Dittrich R, Ditt H, Klotz E, Fischbach R, Ringelstein EB, Heindel W (2007) Color-coded perfused blood volume imaging using multidetector CT: initial results of whole-brain perfusion analysis in acute cerebral ischemia. Eur Radiol. DOI  10.1007/s00330-007-0580-7
  2. 2.
    Lee TY (2002) Functional CT: physiological models. Trends Biotechnol 20:S3–S10CrossRefGoogle Scholar
  3. 3.
    Miles KA (2002) Functional computed tomography. Eur J Radiol 38:2079–2084Google Scholar
  4. 4.
    Sobol WT, Cure JK (2004) Can in vivo assessment of tissue hemodynamics with dynamic contrast-enhanced CT be used in the diagnosis of tumors and monitoring of cancer therapy outcomes? Radiology 232:631–632PubMedCrossRefGoogle Scholar
  5. 5.
    Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, Larsson HBW, Lee T-Y, Mayr NA, Parker GJM, Port RE, Taylor J, Weisskoff RM (1999) Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusible tracer: standardized quantities and symbols. JMRI 10:223–232PubMedCrossRefGoogle Scholar
  6. 6.
    Nabavi DG, Cenic A, Craen RA, Gelb AW, Bennett JD, Kozak R, Lee TY (1999) CT assessment of cerebral perfusion: experimental validation and initial clinical experience. Radiology 213:141–149PubMedGoogle Scholar
  7. 7.
    Cheong LHD, Lim CCT, Koh TS (2004) Dynamic contrast-enhanced CT of intracranial meningioma: Comparison of distributed and compartmental tracer kinetic models-Initial results. Radiology 232:21–30CrossRefGoogle Scholar
  8. 8.
    Roberts HC, Roberts TPL, Lee T-Y, Dillion WP (2002) Dynamic, contrast-enhanced CT of human brain tumors: Quantitative assessment of blood volume, blood flow, and microvascular permeability: Report of two cases. AJNR Am J Neuroradiol 23:828–832PubMedGoogle Scholar
  9. 9.
    Roberts HC, Roberts TPL, Lee T-Y, Dillion WP (2002) Dynamic contrast-enhanced computed tomography (CT) for quantitative estimation of microvascular permeability in human brain tumors. Acad Radiol 9(suppl 2):S364–S367PubMedCrossRefGoogle Scholar
  10. 10.
    Roberts HC, Roberts TPL, Bollen AW, Ley S, Brasch RC, Dillion WP (2001) Correlation of microvascular permeability derived from dynamic contrast-enhanced MR imaging with histologic grade and tumor labeling index: a study in human brain tumors. Acad Radiol 8:384–391PubMedCrossRefGoogle Scholar
  11. 11.
    Lüdemann L, Grieger W, Wurm R, Budzisch M, Hamm B, Zimmer C (2001) Comparison of dynamic contrast-enhanced MRI with WHO tumor grading for gliomas. Eur Radiol 11:1231–1241PubMedCrossRefGoogle Scholar
  12. 12.
    Mayr NA, Hawighorst H, Yuh WTC, Essig M, Magnotta VA, Knopp MV (1999) MR microcirculation assessment in cervical cancer: correlations with histomorphological tumor markers and clinical outcome. JMRI 10:267–276.PubMedCrossRefGoogle Scholar
  13. 13.
    Larson KB, Markham J, Raichle ME (1987) Tracer-kinetic models for measuring cerebral blood flow using externally detected radiotracers. J Cereb Blood Flow Metab 7:443–463PubMedGoogle Scholar
  14. 14.
    Koh TS, Cheong LH, Hou Z, Soh YC (2003) A physiologic model of capillary-tissue exchange for dynamic contrast-enhanced imaging of tumor microcirculation. IEEE Trans Biomed Eng 50:159–167PubMedCrossRefGoogle Scholar
  15. 15.
    Bassingthwaighte JB, Goresky CA (1984) Modeling in the analysis of solute and water exchange in the microvasculature. In: Renkin EM and Michel CC eds. Handbook of Physiology, Section 2: The Cardiovascular System, Volume IV: The Microcirculation, Part 1. Bethesda, MD: American Physiological Society 549–626Google Scholar
  16. 16.
    St. Lawrence KS, Lee TY (1998) An adiabatic approximation to the tissue homogeneity model for water exchange in the brain: I. Theoretical derivation. J Cereb Blood Flow Metab 18:1365–1377PubMedCrossRefGoogle Scholar
  17. 17.
    Johnson JA, Wilson TA (1966) A model for capillary exchange. Am J Physiol 210:1299–1303PubMedGoogle Scholar
  18. 18.
    Renkin EM (1959) Transport of potassium-42 from blood to tissue in isolated mammalian skeletal muscles. Am J Physiol 197:1205–1210PubMedGoogle Scholar
  19. 19.
    Crone C (1965) The permeability of brain capillaries to non-electrolytes. Acta Physiol Scand 64:404–417CrossRefGoogle Scholar
  20. 20.
    Henderson E, Milosevic MF, Haider MA, Yeung IWT (2003) Functional CT imaging of prostate cancer. Phys Med Biol 48:3085–3100PubMedCrossRefGoogle Scholar
  21. 21.
    Calamante F, Gadian DG, Connelly A (2000) Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition. Magn Reson Med 44:466–473PubMedCrossRefGoogle Scholar
  22. 22.
    Tsai D (1995) A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recog Lett 16:653–666CrossRefGoogle Scholar
  23. 23.
    Castleman KR (1996) Digital imaging processing, Prentice Hall 1996Google Scholar
  24. 24.
    Kundel HL, Polansky M (2003) Measurement of observer agreement. Radiology 228:303–308PubMedCrossRefGoogle Scholar
  25. 25.
    Leach MO, Brindle KM, Evelhoch JL et al (2005) The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Brit J Cancer 92:1599–1610PubMedCrossRefGoogle Scholar
  26. 26.
    Konig M, Bultmann E, Bode-Schnurbus L, Koenen D, Mielke E, Heuser L (2007) Image quality in CT perfusion imaging of the brain. The role of iodine concentration. Eur Radiol 17:39–47PubMedCrossRefGoogle Scholar
  27. 27.
    Miles KA, Young H, Chica SL, Esser PD (2007) Quantitative contrast-enhanced computed tomography: is there a need for system calibration? Eur Radiol 17:919–926PubMedCrossRefGoogle Scholar
  28. 28.
    Lee MC, Cha S, Chang SM, Nelson SJ (2005) Dynamic susceptibility contrast perfusion imaging of radiation effects in normal-appearing brain tissue: changes in the first-pass and recirculation phases. J Magn Reson Imaging 21:683–693PubMedCrossRefGoogle Scholar
  29. 29.
    Covarrubias DJ, Rosen BR, Lev MH (2004) Dynamic magnetic resonance perfusion imaging of brain tumors. Oncologist 9:528–537PubMedCrossRefGoogle Scholar
  30. 30.
    Stenberg L, Englund E, Wirestam R, Siesjo P, Salford LG, Larsson EM (2006) Dynamic susceptibility contrast-enhanced perfusion magnetic resonance (MR) imaging combined with contrast-enhanced MR imaging in the follow-up of immunogene-treated glioblastoma multiforme. Acta Radiol 47:852–861PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • S. Bisdas
    • 1
  • X. Yang
    • 2
  • C. C. T. Lim
    • 3
    • 4
  • T. J. Vogl
    • 1
  • T. S. Koh
    • 2
  1. 1.Department of Diagnostic and Interventional RadiologyJohann Wolfgang Goethe University HospitalFrankfurtGermany
  2. 2.School of Electrical and Electronic EngineeringNanyang Technological UniversityNanyangSingapore
  3. 3.Department of NeuroradiologyNational Neuroscience InstituteSingaporeSingapore
  4. 4.Department of Diagnostic Radiology, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore

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