Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain

  • Antoni Gurgui
  • Debora Gil
  • Vicente Grau
  • Enric Marti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10124)

Abstract

Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries.

Keywords

Laplacian Constrained maps Parameterization Basal ring 

References

  1. 1.
    Carapella, V., Bordas, R., Pathmanathan, P., et al.: Quantitative study of the effect of tissue microstructure on contraction in a computational model of rat left ventricle. PloS One 9(4), e92792 (2014)CrossRefGoogle Scholar
  2. 2.
    Casero, R., Burton, R.A.B., Quinn, T.A., Bollensdorff, C., Hales, P., Schneider, J.E., Kohl, P., Grau, V.: Towards high-resolution cardiac atlases: ventricular anatomy descriptors for a standardized reference frame. In: Camara, O., Pop, M., Rhode, K., Sermesant, M., Smith, N., Young, A. (eds.) STACOM 2010. LNCS, vol. 6364, pp. 75–84. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15835-3_8 CrossRefGoogle Scholar
  3. 3.
    Desbrun, M., Meyer, M., Alliez, P.: Intrinsic parameterizations of surface meshes. Comput. Graph. Forum 21, 209–218 (2002)CrossRefGoogle Scholar
  4. 4.
    Evans, L.C.: Partial Differential Equations. American Mathematical Society, Providence (1998)MATHGoogle Scholar
  5. 5.
    Garcia-Barnes, J., Gil, D., Badiella, L., et al.: A normalized framework for the design of feature spaces assessing the left ventricular function. TMI 29(3), 733–745 (2010)Google Scholar
  6. 6.
    Gil, D., et al.: What a difference in biomechanics cardiac fiber makes. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 253–260. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36961-2_29 CrossRefGoogle Scholar
  7. 7.
    Glocker, B., Sotiras, A., Komodakis, N., Paragios, N.: Deformable medical image registration: setting the state of the art with discrete methods*. Ann. Rev. Biomed. Eng. 13, 219–244 (2011)CrossRefGoogle Scholar
  8. 8.
    Gurgui, A., Gil, D., Marti, E.: Laplacian unitary domain for texture morphing. In: VISAPP (2015)Google Scholar
  9. 9.
    Helm, P., Beg, M.F., Miller, M.I., et al.: Measuring and mapping cardiac fiber and laminar architecture using diffusion tensor MR imaging. Ann. N. Y. Acad. Sci. 1047, 296–307 (2005)CrossRefGoogle Scholar
  10. 10.
    Karim, R., et al.: Surface flattening of the human left atrium and proof-of-concept clinical applications. Comput. Med. Imag. Graph 38, 251–266 (2014)CrossRefGoogle Scholar
  11. 11.
    Lamata, P., Niederer, S., Nordsletten, D., et al.: An accurate, fast and robust method to generate patient-specific cubic hermite meshes. Med. Image Anal. 15(6), 801–813 (2011)CrossRefGoogle Scholar
  12. 12.
    Lombaert, H., Grady, L., Pennec, X., Ayache, N., Cheriet, F.: Spectral log-demons: diffeomorphic image registration with very large deformations. IJCV 107(3), 254–271 (2014)CrossRefGoogle Scholar
  13. 13.
    Medrano-Gracia, P., Cowan, B.R., Suinesiaputra, A., et al.: Atlas-based anatomical modeling and analysis of heart disease. Drug Disc. Today Dis. Models 14, 33–39 (2015)CrossRefGoogle Scholar
  14. 14.
    Paun, B., Bijnens, B., Butakoff, C.: Subject independent reference frame for the left ventricular detailed cardiac anatomy. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds.) FIMH 2015. LNCS, vol. 9126, pp. 240–247. Springer, Heidelberg (2015). doi:10.1007/978-3-319-20309-6_28 CrossRefGoogle Scholar
  15. 15.
    Poveda, F., Marti, E., Andaluz, A., et al.: Helical structure of the cardiac ventricular anatomy assessed by diffusion tensor magnetic resonance imaging multi-resolution tractography. Revista Española de Cardiología 66(10), 782–790 (2013)CrossRefGoogle Scholar
  16. 16.
    Soto-Iglesias, D., Butakoff, C., Andreu, D., et al.: Integration of electro-anatomical and imaging data of the left ventricle: an evaluation framework. Med. Image Anal. 32, 131–144 (2016)CrossRefGoogle Scholar
  17. 17.
    Vera, S., Ballester, M.A.G., Gil, D.: Anatomical parameterization for volumetric meshing of the liver. In: SPIE (2014)Google Scholar
  18. 18.
    Vera, S., González, M.A., Linguraru, M.G., Gil, D.: Optimal medial surface generation for anatomical volume representations. In: Yoshida, H., Hawkes, D., Vannier, M.W. (eds.) ABD-MICCAI 2012. LNCS, vol. 7601, pp. 265–273. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33612-6_28 CrossRefGoogle Scholar
  19. 19.
    Young, A.A., Frangi, A.F.: Computational cardiac atlases: from patient to population and back. Exp. Physiol. 94, 578–596 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Antoni Gurgui
    • 1
  • Debora Gil
    • 1
    • 2
  • Vicente Grau
    • 3
  • Enric Marti
    • 2
  1. 1.Computer Vision Center of CatalunyaUniversitat Autonoma de BarcelonaBarcelonaSpain
  2. 2.Computer Sciences DepartmentUniversitat Autonoma de BarcelonaBarcelonaSpain
  3. 3.Department of Engineering Science, Institute of Biomedical EngineeringUniversity of OxfordOxfordUK

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