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4D Cardiac Motion Modeling Using Pair-Wise Mesh Registration

  • Siyeop Yoon
  • Stephen Baek
  • Deukhee LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11395)

Abstract

In this paper, we present a novel method for the real-time cardiac motion compensation. Our method generates interpolated cardiac motion using segmented mesh models from preoperative 3D+T computed tomography angiography (CTA). We propose a pair-wise mesh registration technique for building correspondence and interpolating the control points over a cardiac cycle. The key contribution of this work is a rapid creation of a deformation field through a concise mathematical formulation while maintaining desired properties. These are \(C^2\) continuity, invertibility, incompressibility of cardiac structure and capability to handling large deformation. And we evaluated the proposed method using different conditions, such as deformation resolution, temporal sampling rates, and template model selection.

Keywords

Cardiac Spatiotemporal Registration Deformation 4D motion modeling 

References

  1. 1.
    Baka, N., et al.: Statistical coronary motion models for 2D+t/3D registration of X-ray coronary angiography and CTA. Med. Image Anal. 17(6), 698–709 (2013)CrossRefGoogle Scholar
  2. 2.
    Burger, M., Modersitzki, J., Ruthotto, L.: A hyperelastic regularization energy for image registration. SIAM J. Sci. Comput. 35(1), B132–B148 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Clark, H.: NCDs: a challenge to sustainable human development. Lancet 381(9866), 510–511 (2013)CrossRefGoogle Scholar
  4. 4.
    Giesler, T., et al.: Noninvasive visualization of coronary arteries using contrast-enhanced multidetector CT: influence of heart rate on image quality and stenosis detection. Am. J. Roentgenol. 179(4), 911–916 (2002)CrossRefGoogle Scholar
  5. 5.
    Gilbert, K., Pontre, B., Occleshaw, C., Cowan, B., Suinesiaputra, A., Young, A.: 4D modelling for rapid assessment of biventricular function in congenital heart disease. Int. J. Cardiovasc. Imaging 34(3), 407–417 (2018)CrossRefGoogle Scholar
  6. 6.
    Guyader, J.M., Bernardin, L., Douglas, N.H., Poot, D.H., Niessen, W.J., Klein, S.: Influence of image registration on apparent diffusion coefficient images computed from free-breathing diffusion MR images ofthe abdomen. J. Magn. Reson. Imaging 42(2), 315–330 (2015)CrossRefGoogle Scholar
  7. 7.
    Huang, J., Abendschein, D., Davila-Roman, V.G., Amini, A.A.: Spatio-temporal tracking of myocardial deformations with a 4-D B-spline model from tagged MRI. IEEE Trans. Med. Imaging 18(10), 957–972 (1999)CrossRefGoogle Scholar
  8. 8.
    Metz, C.T., et al.: Patient specific 4D coronary models from ECG-gated CTA data for intra-operative dynamic alignment of CTA with X-ray images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 369–376. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-04268-3_46CrossRefGoogle Scholar
  9. 9.
    Metz, C., Klein, S., Schaap, M., van Walsum, T., Niessen, W.J.: Nonrigid registration of dynamic medical imaging data using nD+ t B-splines and a groupwise optimization approach. Med. Image Anal. 15(2), 238–249 (2011)CrossRefGoogle Scholar
  10. 10.
    Ohnesorge, B.M., Flohr, T.G., Becker, C.R., Knez, A., Reiser, M.F.: Multi-slice and Dual-Source CT in Cardiac Imaging: Principles-Protocols-Indications-Outlook. Springer, Heidelberg (2006).  https://doi.org/10.1007/978-3-540-49546-8CrossRefGoogle Scholar
  11. 11.
    Otto, C.M.: Textbook of Clinical Echocardiography E-Book. Elsevier Health Sciences, St. Louis (2013)Google Scholar
  12. 12.
    Peng, P., Lekadir, K., Gooya, A., Shao, L., Petersen, S.E., Frangi, A.F.: A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magn. Reson. Mater. Phys. Biol. Med. 29(2), 155–195 (2016)CrossRefGoogle Scholar
  13. 13.
    Rohé, M.M., Sermesant, M., Pennec, X.: Low-dimensional representation of cardiac motion using baryncetric subspaces: a new group-wise paradigm for estimation, analysis, and reconstruction. Med. Image Anal. 45, 1–12 (2017)CrossRefGoogle Scholar
  14. 14.
    Tarride, J.E., et al.: A review of the cost of cardiovascular disease. Can. J. Cardiol. 25(6), e195–e202 (2009)CrossRefGoogle Scholar
  15. 15.
    Wachinger, C., Navab, N.: Simultaneous registration of multiple images: similarity metrics and efficient optimization. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1221–1233 (2013)CrossRefGoogle Scholar
  16. 16.
    Wilson, K., Guiraudon, G., Jones, D.L., Peters, T.M.: Mapping of cardiac electrophysiology onto a dynamic patient-specific heart model. IEEE Trans. Med. imaging 28(12), 1870–1880 (2009)CrossRefGoogle Scholar
  17. 17.
    Yabe, T., et al.: The impact of percutaneous coronary intervention using the novel dynamic coronary roadmap system. J. Am. Coll. Cardiol. 71(11), A1103 (2018)CrossRefGoogle Scholar
  18. 18.
    Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Korea University of Science and TechnologySeoulSouth Korea
  2. 2.KISTSeoulSouth Korea
  3. 3.University of IowaIowa CityUSA

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