Advertisement

Influence of the Grid Topology of Free-Form Deformation Models on the Performance of 3D Strain Estimation in Echocardiography

  • Brecht Heyde
  • Daniel Barbosa
  • Piet Claus
  • Frederik Maes
  • Jan D’hooge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7945)

Abstract

Different B-spline grid topologies of free-form-deformation (FFD) models have been proposed for 3D strain estimation in echocardiography: classical FFD models are defined in Cartesian space (CFFD), whereas others adapt an anatomically oriented B-spline grid (AFFD) which allow to model the cardiac motion in a more physiological way. The practical advantage of the latter grid topology remains to be proven for echocardiography. In this work, the performance of both models was therefore directly compared using simulated data. Both motion and strain accuracy were competitive for the CFFD and AFFD model: mean error=0.44mm vs 0.48mm, strain error=9.0% vs 7.3% (radial), 2.4% vs 3.1% (longitudinal), 1.9% vs 2.2% (circumferential). However, moving to an anatomical grid topology appears better suited for cardiac deformation estimation as model complexity and computation time was reduced considerably (1051s vs 595s).

Keywords

Non-rigid registration B-spline grid topology motion strain echocardiography 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Elen, A., Choi, H., Loeckx, D., Gao, H., Claus, P., Suetens, P., Maes, F., D’hooge, J.: 3D cardiac strain estimation using spatio-temporal elastic registration of US images: a feasibility study. IEEE Trans. Med. Imaging 27(11), 1580–1591 (2008)CrossRefGoogle Scholar
  2. 2.
    Heyde, B., Cygan, S., Choi, H., Lesniak-Plewinska, B., Barbosa, D., Elen, A., Claus, P., Loeckx, D., Kaluzynski, K., D’hooge, J.: Regional cardiac motion and strain estimation in three-dimensional echocardiography: A validation study in thick-walled univentricular phantoms. IEEE Trans. Ultrason. Freq. Control 59(4), 668–682 (2012)CrossRefGoogle Scholar
  3. 3.
    De Craene, M., Piella, G., Camara, O., Duchateau, N., Silva, E., Doltra, A., D’hooge, J., Brugada, J., Sitges, M., Frangi, A.: Temporal diffeomorphic free-form deformation: application to motion and strain estimation from 3D echocardiography. Med. Image Analysis 16(2), 427–450 (2012)CrossRefGoogle Scholar
  4. 4.
    Chandrashekara, R., Mohiaddin, R., Rueckert, D.: Analysis of myocardial motion and strain patterns using a cylindrical B-spline transformation model. In: Ayache, N., Delingette, H. (eds.) IS4TM 2003. LNCS, vol. 2673, pp. 88–99. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Deng, X., Denney, T.: Three-dimensional myocardial strain reconstruction from tagged MRI using a cylindrical B-spline model. IEEE Trans. Med. Imaging 23(7), 861–867 (2004)CrossRefGoogle Scholar
  6. 6.
    Li, J., Denney, T.: Left ventricular motion reconstruction with a prolate spheroidal B-spline model. Phys. Med. Biol. 51(3), 517–537 (2006)CrossRefGoogle Scholar
  7. 7.
    Lin, N., Duncan, J.: Generalized robust point matching using an extended free-form deformation model: application to cardiac images. In: ISBI - International Symposium on Biomedical Imaging, USA, April 15-18, vol. 1, pp. 320–323 (2004)Google Scholar
  8. 8.
    Heyde, B., Barbosa, D., Claus, P., Maes, F., D’hooge, J.: Three-dimensional cardiac motion estimation based on non-rigid image registration using a novel transformation model adapted to the heart. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 142–150. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    De Craene, M., Allain, P., Gao, H., Prakosa, A., Marchesseau, S., Somphone, O., Hilpert, L., Manrique, A., Delingette, H., Makram-Ebeid, S., Villain, N., D’hooge, J., Sermesant, M., Saloux, E.: Computational and physical phantom setups for the second cardiac motion analysis challenge (cMAC2). In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 125–133. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Kybic, J., Unser, M.: Fast parametric elastic image registration. IEEE Trans. Image Process. 12(11), 1427–1442 (2003)CrossRefGoogle Scholar
  11. 11.
    Byrd, R., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing 16(5), 1190–1208 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Rueckert, D., Sonoda, L., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Brecht Heyde
    • 1
  • Daniel Barbosa
    • 1
  • Piet Claus
    • 1
  • Frederik Maes
    • 2
  • Jan D’hooge
    • 1
    • 3
  1. 1.Cardiovascular Imaging and DynamicsUniversity of LeuvenLeuvenBelgium
  2. 2.Medical Image ComputingUniversity of LeuvenLeuvenBelgium
  3. 3.Medical Imaging LabNorwegian Institute for Science & TechnologyTrondheimNorway

Personalised recommendations