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An Unbiased Groupwise Registration Algorithm for Correcting Motion in Dynamic Contrast-Enhanced Magnetic Resonance Images

  • Mia Mojica
  • Mehran EbrahimiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

A simple and computationally efficient algorithm for performing unbiased groupwise registration to correct motion in a dataset of contrast-enhanced magnetic resonance (DCE-MR) images is presented. All the DCE-MR images in the sequence are registered simultaneously and updates to the reference are computed using an averaging technique that takes into account all the transformations aligning each image to the current reference. The method is validated both subjectively and quantitatively using an abdominal DCE-MRI dataset. When combined with the normalized gradient field dissimilarity measure, it produced promising results and showed significant improvements compared to those obtained from an existing motion correction approach.

Keywords

DCE-MRI registration Multilevel elastic registration Normalized gradient field Groupwise registration 

Notes

Acknowledgments

This work was supported in part by an NSERC Discovery grant and Deborah Saucier Early Researcher Award for Mehran Ebrahimi. Mia Mojica is supported by an Ontario Trillium Scholarship (OTS). The authors would like to acknowledge Dr. Anne Martel of Sunnybrook Research Institute, Toronto, Ontario, Canada for great discussions and for providing the DCE-MRI data.

References

  1. 1.
    Ebrahimi, M., Lausch, A., Martel, A.L.: A Gauss-newton approach to joint image registration and intensity correction. Comput. Methods Programs Biomed. 112(3), 398–406 (2013)CrossRefGoogle Scholar
  2. 2.
    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. LNCS, vol. 5761, pp. 811–819. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-04268-3_100CrossRefGoogle Scholar
  3. 3.
    Geng, X., Christensen, G.E., Gu, H., Ross, T.J., Yang, Y.: Implicit reference-based group-wise image registration and its application to structural and functional MRI. Neuroimage 47(4), 1341–1351 (2009)CrossRefGoogle Scholar
  4. 4.
    Helm, P.A.: A novel technique for quantifying variability of cardiac anatomy: application to the dyssynchronous failing heart. Ph.D thesis, The Johns Hopkins University 164 (2005)Google Scholar
  5. 5.
    Jansen, M., Kuijf, H., Veldhuis, W., Wessels, F., Van Leeuwen, M., Pluim, J.: Evaluation of motion correction for clinical dynamic contrast enhanced MRI of the liver. Phys. Med. Biol. 62(19), 7556 (2017)CrossRefGoogle Scholar
  6. 6.
    Kim, M., Wu, G., Shen, D.: Groupwise registration of breast DCE-MR images for accurate tumor measurement. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 598–601. IEEE (2011)Google Scholar
  7. 7.
    Lausch, A., Ebrahimi, M., Martel, A.: Image registration for abdominal dynamic contrast-enhanced magnetic resonance images. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 561–565. IEEE (2011)Google Scholar
  8. 8.
    Martel, A., Froh, M., Brock, K., Plewes, D., Barber, D.: Evaluating an optical-flow-based registration algorithm for contrast-enhanced magnetic resonance imaging of the breast. Phys. Med. Biol. 52(13), 3803 (2007)CrossRefGoogle Scholar
  9. 9.
    Modersitzki, J.: FAIR: Flexible Algorithms for Image Registration, vol. 6. SIAM (2009)Google Scholar
  10. 10.
    Mojica, M., Pop, M., Sermesant, M., Ebrahimi, M.: Multilevel non-parametric groupwise registration in Cardiac MRI: application to explanted porcine hearts. In: Pop, M. (ed.) STACOM 2017. LNCS, vol. 10663, pp. 60–69. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75541-0_7CrossRefGoogle Scholar
  11. 11.
    Peyrat, J.M., et al.: A computational framework for the statistical analysis of cardiac diffusion tensors: application to a small database of canine hearts. IEEE Trans. Med. imaging 26(11), 1500–1514 (2007)CrossRefGoogle Scholar
  12. 12.
    Sun, Y., Yan, C.H., Ong, S.-H., Tan, E.T., Wang, S.-C.: Intensity-based volumetric registration of contrast-enhanced MR breast images. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 671–678. Springer, Heidelberg (2006).  https://doi.org/10.1007/11866565_82CrossRefGoogle Scholar
  13. 13.
    Tofts, P.: T1-weighted DCE imaging concepts: Modelling, acquisition and analysis. MAGNETOM Flash 3, 30–39 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

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