Incompressible Biventricular Model Construction and Heart Segmentation of 4D Tagged MRI

  • Albert MontilloEmail author
  • Dimitris Metaxas
  • Leon Axel
Conference paper


Most automated methods for cardiac segmentation are not directly applicable to tagged MRI (tMRI) because they do not handle all of the analysis challenges: tags obscure heart boundaries, low contrast, image artifacts, and radial image planes. Other methods do not process all acquired tMRI data or do not ensure tissue incompressibility. In this chapter, we present a cardiac segmentation method for tMRI which requires no user input, suppresses image artifacts, extracts heart features using 3D grayscale morphology, and constructs a biventricular model from the data that ensures the near incompressibility of heart tissue. We project landmarks of 3D features along curves in the solution to a PDE, and embed biomechanical constraints using the finite element method. Testing on normal and diseased subjects yields an RMS segmentation accuracy of ∼ 2 mm, comparing favorably with manual segmentation, interexpert variability and segmentation methods for nontagged cine MRI.


Incompressible biventricular model Mesh construction FEM tMRI 


  1. 1.
    Axel L, Dougherty L. Heart wall motion: improved method of spatial modulation of magnetization for MR imaging. Radiology. 1989;172:349–350.Google Scholar
  2. 2.
    Zerhouni E, Parish D, Rogers W, Yang A, Shapiro E. Human heart: tagging with MR imaging-a method for non-invasive assessment of myocardial motion. Radiology. 1988;169:59–63.Google Scholar
  3. 3.
    Haber I, Metaxas D, Axel L. Three-dimensional motion reconstruction and analysis of the right ventricle using tagged MRI. Med Image Anal. 2000;4(4):335–355.CrossRefGoogle Scholar
  4. 4.
    Tustison N, Amini A. Myocardial kinematics based on tagged MRI from volumetric NURBS models. In: SPIE. vol. 5369; 2003.Google Scholar
  5. 5.
    Young A, Axel L. Three-dimensional motion and deformation of the heart wall: estimation with spatial modulation of magnetization–a model-based approach. Radiology. 1992;185:241–247.Google Scholar
  6. 6.
    Osman N, Prince J. Angle images for measuring heart motion from tagged MRI. In: ICIP; 1998. p.704–8.Google Scholar
  7. 7.
    Young A, Fayad Z, Axel L. Right ventricular midwall surface motion and deformation using magnetic resonance tagging. Am J Physiol. 1996;271:H2677–H2688.Google Scholar
  8. 8.
    Hu Z, Metaxas D, Axel L. In vivo strain and stress estimation of the heart left and right ventricles from MRI images. Med Image Anal. 2003;7(4):435–444.CrossRefGoogle Scholar
  9. 9.
    Dornier C, Ivancevic M, Lecoq G, Osman N, Foxall D, Righetti A, et al. Assessment of the left ventricle ejection fraction by MRI tagging. In: ISMRM; 2002.Google Scholar
  10. 10.
    Guttman M, Prince J, McVeigh E. Tag and Contour Detection in Tagged MR Images of the Left Ventricle. IEEE Trans Med Imaging. 1994;13(1):74–88.CrossRefGoogle Scholar
  11. 11.
    Montillo A, Metaxas D, Axel L. Automated deformable model-based segmentation of the left and right ventricles in tagged cardiac MRI. In: MICCAI; 2003. p. 507–515.Google Scholar
  12. 12.
    Qian Z, Metaxas D, Axel L. Boosting and nonparametric based tracking of tagged MRI cardiac boundaries. In: MICCAI; 2006. p. 636–644.Google Scholar
  13. 13.
    Huang J, Huang X, Metaxas D, Axel L. Adaptive Metamorphs Model for 3D Medical Image Segmentation. In: MICCAI; 2007.Google Scholar
  14. 14.
    Sundar H, Davatzikos C, Biros G. Biomechanically-Constrained 4D Estimation of Myocardial Motion. In: MICCAI; 2009.Google Scholar
  15. 15.
    Chandrashekara R, Mohiaddin R, Razavi R, Rueckert D. Nonrigid Image Registration with Subdivision Lattices: Application to Cardiac MR Image Analysis. In: MICCAI; 2007.Google Scholar
  16. 16.
    Zhang S, Wang X, Metaxas D, Chen T, Axel L. LV surface reconstruction from sparse tMRI using laplacian surface deformation and optimization. In: ISBI; 2009. p. 698–701.Google Scholar
  17. 17.
    Yang L, Georgescu B, Zheng Y, Meer P, Comaniciu D. 3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers. In: CVPR; 2008.Google Scholar
  18. 18.
    Zhu Y, Papademetris X, Sinusas A, Duncan JS. Segmentation of myocardial volumes from real-time 3D echocardiography using an incompressibility constraint. MICCAI. 2007;10(Pt 1): 44–51.Google Scholar
  19. 19.
    Ecabert O, Peters J, Schramm H, Lorenz C, Von Berg J, Walker MJ, et al. Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging. 2008; 27(9):1189–1202.CrossRefGoogle Scholar
  20. 20.
    Bistoquet A, Oshinski J, Skrinjar O. Myocardial deformation recovery from cine MRI using a nearly incompressible biventricular model. Med Image Anal. 2008;12(1):69–85.CrossRefGoogle Scholar
  21. 21.
    Zhuge Y, Udupa J, Liu J, Saha P, Iwanaga T. A Scale-Based Method for Correcting Background Intensity Variation in Acquired Images. In: SPIE; 2002. p. 1103–1111.Google Scholar
  22. 22.
    Nyul L, Udupa J, Zhang X. New variants of a method of MRI scale standardization. IEEE Trans Med Imaging. 2000;19(2):143–150.CrossRefGoogle Scholar
  23. 23.
    Herman G, Zheng J, Bucholtz C. Shape-based interpolation. IEEE Comput Graph. 1992;p. 69–80.Google Scholar
  24. 24.
    Xu C, Prince J. Generalized Gradient Vector Flow external forces for active contours. Sig Proc. 1998;71:131–9.zbMATHCrossRefGoogle Scholar
  25. 25.
    Cook R, Malkus D, M P. Concepts and applications of finite element analysis. Wiley; 1989.Google Scholar
  26. 26.
    Macneal R. Finite Elements: Their Design and Performance. Marcel Dekker; 1994.Google Scholar
  27. 27.
    Qian Z, Metaxas D, Axel L. Learning methods in segmentation of tMRI. ISBI; 2007. p. 688–691.Google Scholar
  28. 28.
    Herzka D, Guttman M, McVeigh E. Myocardial tagging with SSFP. Magn Reson Med. 2003;49(6):329–340.CrossRefGoogle Scholar
  29. 29.
    Ryf S, Spiegel MA, Gerber M, Boesiger P. Myocardial tagging with 3D-CSPAMM. J of Mag Res Imaging. 2002;16:320–325.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.GE Global Research CenterNiskayunaUSA
  2. 2.Department of Computer ScienceRutgers UniversityPiscatawayUSA

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