Diffeomorphic Cardiac Motion Estimation with Anisotropic Regularization along Myofiber Orientation

  • Zhijun Zhang
  • David J. Sahn
  • Xubo Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7359)


Quantitative motion analysis from cardiac imaging is an important yet challenging problem. Most of the existing cardiac motion estimation methods ignore the fact that the myocardium is a fibrous structure with elastic anisotropy. We propose a novel method in which an anisotropic regularization energy is used to favor the motion consistency with the myofiber orientation. The myofiber direction comes from a diffusion tensor image and it is mapped to the end-diastole frame by using nonrigid registration. We implement the method based on a diffeomorphic motion estimation framework in which a spatiotemporally smooth velocity field is estimated by optimization of a variational energy. We validate the proposed method by using cine magnetic resonance imaging (MRI) datasets and echocardiography of an open-chest pig with sonomicrometry. We compare the proposed method with a temporal diffeomorphic free form deformation method without consideration of myofiber orientation. Experiments results show that the proposed motion estimation method has higher accuracy.


Diffeomorphic registration nonrigid registration motion estimation myofiber orientation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Buckberg, G., Hoffman, J.I.E., Mahajan, A., Saleh, S., Coghlan, C.: Cardiac Mechanics Revisited The Relationship of Cardiac Architecture to Ventricular Function. Circulation 118, 2571–2587 (2008)CrossRefGoogle Scholar
  2. 2.
    Elen, A., Choi, H.F., Loeckx, D., Gaom, H., Claus, P., Suetens, P., Maes, F., D’hooge, J.: Three-dimensional cardiac strain estimation using spatio-temporal elastic registration of ultrasound images: a feasibility study. IEEE Trans. Med. Imag. 27(11), 1580–1591 (2008)CrossRefGoogle Scholar
  3. 3.
    Rougon, N., Petitjean, C., Preteux, F., Cluzel, P., Grenier, P.: A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures. Med.l Imag. Anal. 9(4), 353–375 (2005)CrossRefGoogle Scholar
  4. 4.
    Ledesma-Carbayo, M.J., Mah-Casado, P., Santos, A., Prez-David, E., GarMA, D.M.: Spatio-Temporal Nonrigid Registration for Ultrasound Cardiac Motion Estimation. IEEE Trans. Med. Imag. 24(9), 1113–1126 (2005)CrossRefGoogle Scholar
  5. 5.
    Metz, C.T., Klein, S., Schaap, M., Walsum, T., Niessen, W.J.: Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach. Med. Imag. Anal. 15(2), 238–249 (2011)CrossRefGoogle Scholar
  6. 6.
    Castillo, E., Castillo, R., Martinez, J., Shenoy, M., Guerrero, T.: Four-dimensional deformable image registration using trajectory modeling. Physics in Medicine and Biology 55(1), 305–327 (2010)CrossRefGoogle Scholar
  7. 7.
    Sundar, H., Littb, H., Shen, D.G.: Estimating myocardial motion by 4D image warping. Pattern Recognition 42, 2514–2526 (2009)CrossRefGoogle Scholar
  8. 8.
    Skrinjar, O., Bistoquet, A., Tagare, H.: Symmetric and Transitive Registration of Image Sequences. In: IJBI (2008)Google Scholar
  9. 9.
    Beg, M.F., Miller, M.I., Trouve, A., Younes, L.: Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. IJCV 61(2), 139–157 (2005)CrossRefGoogle Scholar
  10. 10.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic Demons: Efficient Non-parametric Image Registration. NeuroImage 45(1,S1), 61–72 (2009)Google Scholar
  11. 11.
    Khan, A.R., Beg, M.F.: Representation of time-varying shapes in the large deformation diffeomorphic framework. In: ISBI 2008, pp. 1521–1524 (2008)Google Scholar
  12. 12.
    Craene, M.D., Piella, G., Camaraa, O., Duchateaua, N., Silvae, E., Doltrae, A., D’hooge, J., Brugadae, J., Sitgese, M., Frangi, A.F.: Temporal diffeomorphic free-form deformation: application to motion and strain estimation from 3D echocardiography. Med. Imag. Anal. 16(1), 427–450 (2012)CrossRefGoogle Scholar
  13. 13.
    Bistoquet, A., Oshinski, J., Skrinjar, O.: Myocardial deformation recovery from cine MRI using a nearly incompressible biventricular model. Med. Imag. Anal. 12(1), 69–85 (2008)CrossRefGoogle Scholar
  14. 14.
    Mansi, T., Pennec, X., Sermesant, M., Delingette, H., Ayache, N.: iLogDemons: A demons-based registration algorithm for tracking incompressible elastic biological tissues. International Journal of Computer Vision (IJCV) 92(1), 92–111 (2011)CrossRefGoogle Scholar
  15. 15.
    Sengupta, P.P., Tajik, A.J., Chandrasekaran, K., Khandheria, B.K.: Twist Mechanics of the Left Ventricle. JACC 1(3), 366–376 (2008)Google Scholar
  16. 16.
    Rademakers, F.E., Rogers, W.J., Guier, W.H., Hutchins, G.M., Siu, C.O., Weisfeldt, M.L., Weiss, J.L., Shapiro, E.P.: Relation of regional cross-fiber shortening to wall thickening in the intact heart. Three-dimensional strain analysis by NMR tagging. Circulation 89(3), 1174–1182 (1994)Google Scholar
  17. 17.
    Ubbink, S.W.J., Bovendeerda, P.H.M., Delhaasb, T., Artsa, T., Vossea, F.N.: Towards model-based analysis of cardiac MR tagging data: Relation between left ventricular shear strain and myofiber orientation. Med. Imag. Anal. 10(4), 632–641 (2006)CrossRefGoogle Scholar
  18. 18.
    Sermesant, M., Forest, C., Pennec, X., Delingette, H., Ayache, N.: Deformable biomechanical models: Application to 4D cardiac image analysis. Med. Imag. Anal. 7(4), 475–488 (2003)CrossRefGoogle Scholar
  19. 19.
    Papademetris, X., Sinusas, A.J., Dione, D.P., Duncan, J.S.: Estimation of 3D left ventricular deformation from echocardiography. Med. Imag. Anal. 5(1), 17–28 (2001)CrossRefGoogle Scholar
  20. 20.
    Dupuis, P., Grenander, U.: Variational problems on flows of diffeomorphisms for image matching. Quarterly Appl. Math. 56(3), 587–600 (1998)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage 38(1), 95–113 (2007)CrossRefGoogle Scholar
  22. 22.
    Rueckert, D., Aljabar, P., Heckemann, R.A., Hajnal, J.V., Hammers, A.: Diffeomorphic Registration Using B-Splines. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006, Part II. LNCS, vol. 4191, pp. 702–709. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  23. 23.
    Meunier, J.: Tissue motion assessment from 3D echographic speckle tracking. Phys. Med. Biol. 43, 1241–1254 (1998)CrossRefGoogle Scholar
  24. 24.
    Peyrat, J.M., Sermesant, M., Pennec, X., Delingette, H., Xu, C.Y., McVeigh, E.R., Ayache, N.: A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts. IEEE Trans. Med. Imag. 26(11), 1500–1514 (2007)CrossRefGoogle Scholar
  25. 25.
    Fonseca, C.G., Backhaus, M., Lima, J.A.C., Medrano-Gracia, P., Shivkumar, K., Suinesiaputra, A., Tao, W., Young, A.A.: The Cardiac Atlas Project C An imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16), 2288–2295 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhijun Zhang
    • 1
  • David J. Sahn
    • 1
    • 2
  • Xubo Song
    • 1
  1. 1.Department of Biomedical EngineeringOregon Health and Science UniversityBeavertonUSA
  2. 2.Department of Pediatric CardiologyOregon Health and Science UniversityBeavertonUSA

Personalised recommendations