Advertisement

An Incompressible Log-Domain Demons Algorithm for Tracking Heart Tissue

  • Kristin McLeod
  • Adityo Prakosa
  • Tommaso Mansi
  • Maxime Sermesant
  • Xavier Pennec
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7085)

Abstract

We describe an application of the previously proposed iLogDemons algorithm to the STACOM motion-tracking challenge data. The iLogDemons algorithm is a consistent and efficient framework for tracking left-ventricle heart tissue using an elastic incompressible non-linear registration algorithm based on the LogDemons algorithm. This method has shown promising results when applied to previous data-sets. Along with having the advantages of the LogDemons algorithm such as computing deformations that are invertible with smooth inverse, the method has the added advantage of allowing physiological constraints to be added to the deformation model. The registration is entirely performed in the log-domain with the incompressibility constraint strongly ensured and applied directly in the demons minimisation space. Strong incompressibility is ensured by constraining the stationary velocity fields that parameterise the transformations to be divergence-free in the myocardium. The method is applied to a data-set of 15 volunteers and one phantom, each with echocardiography, cine-MR and tagged-MR images. We are able to obtain reasonable results for each modality and good results for echocardiography images with respect to quality of the registration and computed strain curves.

Keywords

Registration Algorithm Echocardiography Image Physiological Constraint Incompressibility Constraint Segmentation Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mansi, T., Pennec, X., Sermesant, M., Delingette, H., Ayache, N.: Ilogdemons: A demons-based registration algorithm for tracking incompressible elastic biological tissues. Int. J. of Computer Vision (2011)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A Log-Euclidean Framework for Statistics on Diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006, Part I. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Dru, F., Vercauteren, T.: An ITK implementation of the symmetric log-domain diffeomorphic demons algorithm. Insight Journal (January-June 2009)Google Scholar
  5. 5.
    Simard, P.Y., Mailloux, G.E.: A projection operator for the restoration of divergence-free vector fields. IEEE Transaction on Pattern Analysis and Machine Intelligence 10, 248–256 (1988)CrossRefGoogle Scholar
  6. 6.
    Saad, Y.: Iterative methods for sparse linear systems. Society for Industrial Mathematics, vol. 73. PWS (2003)Google Scholar
  7. 7.
    Mansi, T.: Image-Based Physiological and Statistical Models of the Heart, Application to Tetralogy of Fallot. Thèse de sciences (phd thesis), Ecole Nationale Supérieure des Mines de Paris (September 2010)Google Scholar
  8. 8.
    Moore, C., Lugo-Olivieri, C., McVeigh, E., Zerhouni, E.: Three-dimensional systolic strain patterns in the normal human left ventricle: Characterization with tagged MR imaging. Radiology 214, 453–466 (2000)CrossRefGoogle Scholar
  9. 9.
    Glass, L., Hunter, P., McCulloch, A.: Theory of Heart: Biomechanics, Biophysics, and Nonlinear Dynamics of Cardiac Function. Springer, Heidelberg (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kristin McLeod
    • 1
  • Adityo Prakosa
    • 1
  • Tommaso Mansi
    • 2
  • Maxime Sermesant
    • 1
  • Xavier Pennec
    • 1
  1. 1.INRIA Méditerranée, Asclepios ProjectSophia AntipolisFrance
  2. 2.Image Analytics and InformaticsSiemens Corporate ResearchPrincetonU.S.A.

Personalised recommendations