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Multilevel Non-parametric Groupwise Registration in Cardiac MRI: Application to Explanted Porcine Hearts

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Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges (STACOM 2017)

Abstract

Statistical atlases of myocardial fiber directions have great utility in modelling applications. The first step in building atlases requires a registration of the hearts to a template. In this paper, we performed groupwise registration on a small database of explanted pig hearts (\(N=4\)) and coupled it with a multilevel pairwise registration framework in order to generate an average cardiac geometry. The scheme implemented in our experiments effectively registers and normalizes the hearts despite a high variability in cardiac measurements. In addition, we adopted an intuitive averaging technique on the transformed versions of each heart to obtain a new reference geometry at every iteration. This reduces biases that may be introduced by the selection of an initial reference geometry in the construction of an average cardiac geometry. The next step will focus on improving current results by using a larger database of heart samples.

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References

  1. World Health Organization: Cardiovascular Diseases (2017)

    Google Scholar 

  2. Peyrat, J.M., Sermesant, M., Pennec, X., et al.: A computational framework for the statistical analysis of cardiac diffusion tensors: application to a small database of canine hearts. IEEE Trans. Med. Imag. 26, 1500–14 (2007)

    Article  Google Scholar 

  3. Lombaert, H., Peyrat, J.M., Croisille, P., et al.: Human atlas of the cardiac fiber architecture: study on a healthy population. IEEE Trans. Med. Imag. 31(7), 1436–47 (2012)

    Article  Google Scholar 

  4. Avants, B., Gee, J.C.: Shape averaging with diffeomorphic flows for atlas creation. In: 2nd IEEE International Symposium on Biomedical Imaging, vol. 1, 595–598 (2004)

    Google Scholar 

  5. Pop, M., Ghugre, N.R., Ramanan, V., et al.: Quantification of fibrosis in infarcted swine hearts by ex vivo late gadolinium-enhancement and diffusion-weighted MRI methods. Phys. Med. Biol. 58(15), 5009–28 (2013)

    Article  Google Scholar 

  6. Beg, M.F., Helm, P.A., McVeigh, E., Miller, M.I., Winslow, R.L.: Computational cardiac anatomy Using MRI. Magn. Reson. Med. Off. J. Soc. Magn. Reson. Med./ Soc. Magn. Reson. Med. 52(5), 1167–1174 (2004)

    Article  Google Scholar 

  7. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Symmetric log-domain diffeomorphic registration: a demons-based approach. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 754–761. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85988-8_90

    Chapter  Google Scholar 

  8. Helm, P.: A Novel Technique for Quantifying Variability of Cardiac Anatomy: Application to the Dyssynchronous Failing Heart. Johns Hopkins University, Baltimore (2005)

    Google Scholar 

  9. Modersitzki, J.: Numerical Methods for Image Registration. Oxford University Press, Oxford (2004)

    MATH  Google Scholar 

  10. Modersitzki, J.: FAIR: Flexible Algorithms for Image Registration. SIAM, Philadelphia (2009)

    Book  MATH  Google Scholar 

  11. Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer, New York (2006)

    MATH  Google Scholar 

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Correspondence to Mehran Ebrahimi .

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Mojica, M., Pop, M., Sermesant, M., Ebrahimi, M. (2018). Multilevel Non-parametric Groupwise Registration in Cardiac MRI: Application to Explanted Porcine Hearts. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-75541-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75540-3

  • Online ISBN: 978-3-319-75541-0

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