Deformable Image Registration and Intensity Correction of Cardiac Perfusion MRI

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8896)

Abstract

Dynamic contrast Magnetic Resonance myocardial perfusion imaging has evolved into an accurate technique for the diagnosis of coronary artery disease. In this manuscript, we introduce and evaluate the performance of a non-rigid joint multi-level image registration and intensity correction algorithm on a common dataset. An objective functional is formed for which the corresponding Hessian and Jacobian is computed and employed in a multi-level Gauss-Newton minimization approach. In this paper, our experiments are based on elastic regularization on the transformation and total variation on the intensity correction. Our preliminary validations suggest that the registration scheme provides suitable motion correction if the parameters in the algorithm are properly tuned.

Keywords

Image registration Inverse problems Intensity correction Optimization Multi-level 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys 24(4), 325–376 (1992)CrossRefGoogle Scholar
  2. 2.
    Buonaccorsi, G.A., O’Connor, J.P.B., Caunce, A., Roberts, C., Cheung, S., Watson, Y., Davies, K., Hope, L., Jackson, A., Jayson, G.C., Parker, G.J.M.: Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data. Magnetic Resonance in Medicine 58(5), 1010–1019 (2007)CrossRefGoogle Scholar
  3. 3.
    DiBella, E.V.R., et al.: The effect of obesity on regadenoson-induced myocardial hyperemia: a quantitative magnetic resonance imaging study. The International Journal of Cardiovascular Imaging 28(6), 1435–1444 (2012)CrossRefGoogle Scholar
  4. 4.
    Ebrahimi, M., Lausch, A., Martel, A.L.: A gauss-newton approach to joint image registration and intensity correction. Computer Methods and Programs in Biomedicine 112(3), 398–406 (2013)CrossRefGoogle Scholar
  5. 5.
    Ebrahimi, M., Martel, A.L.: A general pde-framework for registration of contrast enhanced images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 811–819. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Fischer, B., Modersitzki, J.: Ill-posed medicine - an introduction to image registration. Inverse Problems 24, 1–19 (2008)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Ardeshir Goshtasby, A.: 2-D and 3-D Image Registration. Wiley Press, New York (2005)Google Scholar
  8. 8.
    Haber, E., Modersitzki, J.: A multilevel method for image registration. SIAM J. Sci. Comput. 27(5), 1594–1607 (2006)CrossRefMathSciNetMATHGoogle Scholar
  9. 9.
    Hajnal, J., Hawkes, D., Hill, D.: Medical Image Registration. CRC Press (2001)Google Scholar
  10. 10.
    Hill, D.L.G., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Physics in Medicine and Biology 46, R1–R45 (2001)CrossRefGoogle Scholar
  11. 11.
    Lausch, A., Ebrahimi, M., Martel, A.: Image registration for abdominal dynamic contrast-enhance magnetic resonance images. In: 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 561–565 (2011)Google Scholar
  12. 12.
    Modersitzki, J.: Numerical methods for image registration. Oxford University Press, Oxford (2004)MATHGoogle Scholar
  13. 13.
    Modersitzki, J.: (FAIR) Flexible Algorithms for Image Registration. SIAM (2009)Google Scholar
  14. 14.
    Modersitzki, J., Wirtz, S.: Combining homogenization and registration. In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 257–263. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer (2006)Google Scholar
  16. 16.
    Pack, N., DiBella, E.V.R.: Comparison of myocardial perfusion estimates from dynamic contrast-enhanced magnetic resonance imaging with four quantitative analysis methods. Magnetic Resonance in Medicine 64(1), 125–137 (2010)CrossRefGoogle Scholar
  17. 17.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(1–4), 259–268 (1992)CrossRefMATHGoogle Scholar
  18. 18.
    Vogel, C.R.: Computational Methods for Inverse Problems. SIAM (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of ScienceUniversity of Ontario Institute of TechnologyOshawaCanada

Personalised recommendations