Advertisement

T1 Mapping, AIF and Pharmacokinetic Parameter Extraction from Dynamic Contrast Enhancement MRI Data

  • Gilad Liberman
  • Yoram Louzoun
  • Olivier Colliot
  • Dafna Ben Bashat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7012)

Abstract

Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a sensitive, noninvasive technique for the assessment of microvascular properties of the tissue. Quantitative physiological parameters can be obtained using pharmacokinetic (PK) models that track contrast agents as it passes through the tissue vasculature. Such analysis usually requires prior knowledge of the voxels’ T 1 values and of the Arterial Input Function (AIF). Therefore, relaxometry T 1 measurements are usually performed prior to contrast-agent injection and the AIF is manually or automatically extracted from the dynamic data. In this study, a method for a fully automatic analysis of DCE data for joint PK parameters, T 1 mapping and AIF extraction is proposed. Results are shown on simulated data compared to other methods and on data acquired from healthy subjects and patients with Glioblastoma who received anti-angiogenic therapy. The proposed method renders DCE analysis to be robust and easily applicable.

Keywords

Signal Ratio Arterial Input Function Contrast Agent Concentration Dynamic Contrast Enhance Dynamic Contrast Enhancement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    O’Connor, J.P.B., Jackson, A., Parker, G.J.M., Jayson, G.C.: DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. Brit. J. Can. 96, 189–195 (2007)CrossRefGoogle Scholar
  2. 2.
    Murase, K.: Efficient method for calculating kinetic parameters using T1-weighted dynamic contrast-enhanced magnetic resonance imaging. Mag. Res. Med. 51, 858–862 (2004)CrossRefGoogle Scholar
  3. 3.
    Fletcher, R.: A modified Marquardt subroutine for nonlinear least squares. Atom. Res. Est. AERE-R6799 (1971)Google Scholar
  4. 4.
    Cardoso, M.F., Salcedo, R., Feyo de Azevedo, S.: The simplex-simulated annealing approach to continuous non-linear optimization. Comp. Chem. Eng. 20, 1065–1080 (1996)CrossRefGoogle Scholar
  5. 5.
    Fluckiger, J.U., Schabel, M.C., DiBella, E.V.R.: Model-based blind estimation of kinetic parameters in dynamic contrast enhanced (DCE)-MRI. Mag. Res. Med. 62, 1477–1486 (2009)CrossRefGoogle Scholar
  6. 6.
    Bouman, C. A., Shapiro, M., Cook, G. W., Atkins, C. B., Cheng, H.: Cluster: An unsupervised algorithm for modeling gaussian mixtures, https://engineering.purdue.edu/~bouman/
  7. 7.
    Parker, G.J., Roberts, C., Macdonald, A., Buonaccorsi, G.A., Cheung, S., Buckley, D.L., Jackson, A., Watson, Y., Davies, K., Jayson, G.C.: Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Mag. Res. Med. 56, 993–1000 (2006)CrossRefGoogle Scholar
  8. 8.
    Deoni, S.C., Rutt, B.K., Peters, T.M.: Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Mag. Res. Med. 49, 515–526 (2003)CrossRefGoogle Scholar
  9. 9.
    Tofts, P.S., Kermode, A.G.: Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Mag. Res. Med. 17, 357–367 (1991)Google Scholar
  10. 10.
    Tofts, P.S., Brix, G., Buckley, D.L., Evelhoch, J.L., Henderson, E., Knopp, M.V., Larsson, H.B., Lee, T.-Y., Mayr, N.A., Parker, G.J., Port, R.E., Taylor, J., Weisskoff, R.M.: Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: Standardized quantities and symbols. J. Mag. Res. Imag. 10, 223–232 (1999)CrossRefGoogle Scholar
  11. 11.
    Ashburner, J., Friston, K.J.: Unied segmentation. NeuroImage 26, 839–851 (2005)CrossRefGoogle Scholar
  12. 12.
    Yang, C., Karczmar, G.S., Medved, M., Stadler, W.M.: Multiple reference tissue method for contrast agent arterial input function estimation. Mag. Res. Med. 58, 1266–1275 (2007)CrossRefGoogle Scholar
  13. 13.
    Srikanchana, R., Thomasson, D., Choyke, P., Dwyer, A.: A Comparison of Pharmacokinetic Models of Dynamic Contrast Enhanced MRI. In: 17th IEEE Symposium on Computer-Based Medical Systems, p. 361. IEEE Computer Society, Los Alamitos (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gilad Liberman
    • 1
    • 3
    • 4
  • Yoram Louzoun
    • 2
  • Olivier Colliot
    • 3
  • Dafna Ben Bashat
    • 4
  1. 1.Gonda Multidisciplinary Brain Research CenterBar Ilan UniversityRamat GanIsrael
  2. 2.Department of MathematicsBar Ilan UniversityRamat GanIsrael
  3. 3.CNRS UMR 7225, Inserm UMR S 975, Centre de Recherche de l’Institut Cerveau-Moelle (CRICM)Université Pierre et Marie Curie-Paris 6ParisFrance
  4. 4.Functional Brain center, The Wohl Institute for Advanced ImagingTel Aviv Sourasky Medical CenterTel AvivIsrael

Personalised recommendations