A Fast Method of Generating Pharmacokinetic Maps from Dynamic Contrast-Enhanced Images of the Breast

  • Anne L. Martel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


A new approach to fitting pharmacokinetic models to DCE-MRI data is described. The method relies on fitting individual concentration curves to a small set of basis functions and then making use of a look up table to relate the fitting coefficients to pre-calculated pharmacokinetic parameters. This is significantly faster than traditional non-linear fitting methods. Using simulated data and assuming a Tofts model, the accuracy of this direct approach is compared to the Levenberg-Marquardt algorithm. The effect of signal to noise ratio and the number of basis functions used on the accuracy is investigated. The basis fitting approach is slightly less accurate than the traditional non-linear least squares approach but the ten-fold improvement in speed makes the new technique useful as it can be used to generate pharmacokinetic maps in a clinically acceptable timeframe.


Signal Intensity Curve Concentration Time Curf Toft Model Acceptable Timeframe Extracellular Extravascular Volume 


  1. 1.
    Warner, E., et al.: Surveillance of BRCA1 and BRCA2 mutation carriers with magnetic resonance imaging, ultrasound, mammography, and clinical breast examination. JAMA 292(11), 1317–1325 (2004)CrossRefGoogle Scholar
  2. 2.
    Brown, J., et al.: Magnetic resonance imaging screening in women at genetic risk of breast cancer: imaging and analysis protocol for the UK multicentre study. Magnetic Resonance Imaging 18(7), 765–776 (2000)CrossRefGoogle Scholar
  3. 3.
    Warren, R.M., et al.: Reading protocol for dynamic contrast-enhanced MR images of the breast: sensitivity and specificity analysis. Radiology 236(3), 779–788 (2005)CrossRefGoogle Scholar
  4. 4.
    Kuhl, C.K., et al.: Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology 211, 101–110 (1999)Google Scholar
  5. 5.
    Heywang, S.H., et al.: MR imaging of the breast with Gd-DTPA: use and limitations. Radiology 171(1), 95–103 (1989)Google Scholar
  6. 6.
    Kaiser, W.A., Zeitler, E.: MR imaging of the breast: fast imaging sequences with and without Gd-DTPA. Preliminary observations. Radiology 170, 681–686 (1989)Google Scholar
  7. 7.
    Gibbs, P., et al.: Differentiation of benign and malignant sub-1 cm breast lesions using dynamic contrast enhanced MRI. Breast 13(2), 115–121 (2004)CrossRefGoogle Scholar
  8. 8.
    Brix, G., et al.: Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. J. Comput. Assist. Tomogr. 15(4), 621–628 (1991)CrossRefGoogle Scholar
  9. 9.
    Tofts, P.S., Berkowitz, B., Schnall, M.D.: Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model. Magn. Reson Med. 33, 564–568 (1995)CrossRefGoogle Scholar
  10. 10.
    Press, W.H., et al.: Numerical Recipes in C. Cambridge University Press, Cambridge (1994)Google Scholar
  11. 11.
    Ahearn, T.S., et al.: The use of the Levenberg-Marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data. Phys. Med. Biol. 50(9), N85–92 (2005)Google Scholar
  12. 12.
    Buckley, D.L., et al.: Quantitative analysis of multi-slice Gd-DTPA enhanced dynamic MR images using an automated simplex minimization procedure. Magn. Reson. Med. 32(5), 646–651 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anne L. Martel
    • 1
  1. 1.Department of Medical BiophysicsUniversity of TorontoCanada

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