The Cardiac Atlas Project: Preliminary Description of Heart Shape in Patients with Myocardial Infarction

  • Pau Medrano-Gracia
  • Brett R. Cowan
  • J. Paul Finn
  • Carissa G. Fonseca
  • Alan H. Kadish
  • Dan C. Lee
  • Wenchao Tao
  • Alistair A. Young
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6364)

Abstract

The Cardiac Atlas Project seeks to establish a standardized database of cardiovascular imaging examinations, together with derived analyses, for the purposes of statistical characterization of global and regional heart function abnormalities. We present preliminary results from a subset of cases contributed from the Defibrillators to Reduce Risk by Magnetic Resonance Imaging Evaluation (DETERMINE) study of patients with myocardial infarction. Finite element models were fitted to the epicardial and endocardial surfaces throughout the cardiac cycle in 200 patients using a standardized procedure. The control points of the shape model were used in a principal component analysis of shape and motion. The modes were associated with well-known clinical indices of adverse remodeling in heart disease, including heart size, sphericity and mitral valve geometry. These results therefore show promise for the clinical application of a statistical analysis of shape and motion in patients with myocardial infarction.

Keywords

Statistical Shape Model Principal Component Analysis Cardiac Magnetic Resonance Imaging (MRI) Finite Element Modeling 

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References

  1. [Augenstein and Young, 2001]
    Augenstein, K., Young, A.: Finite element modeling for three-dimensional motion reconstruction and analysis. In: Measurement of Cardiac Deformations from MRI: Physical and Mathematical Models, pp. 37–58 (2001)Google Scholar
  2. [Cardoso, 1999]
    Cardoso, J.: High-order contrasts for independent component analysis. Neural computation 11(1), 157–192 (1999)CrossRefMathSciNetGoogle Scholar
  3. [Frangi et al., 2001]
    Frangi, A., Niessen, W., Viergever, M.: Three-dimensional modeling for functional analysis of cardiac images: A review. IEEE Transactions on medical imaging 20(1), 2–5 (2001)CrossRefGoogle Scholar
  4. [Frangi et al., 2002]
    Frangi, A., Rueckert, D., Schnabel, J., Niessen, W.: Automatic construction of multiple-object three-dimensional statistical shape models: Application to cardiac modeling. IEEE Transactions on Medical Imaging 21(9), 1151–1166 (2002)CrossRefGoogle Scholar
  5. [Kadish et al., 2009]
    Kadish, A., Bello, D., Finn, J., Bonow, R., Schaechter, A., Subacius, H., Albert, C., Daubert, J., Fonseca, C., Goldberger, J.: Rationale and design for the defibrillators to reduce risk by magnetic resonance imaging evaluation (DETERMINE) trial. Journal of Cardiovascular Electrophysiology 20(9), 982–987 (2009)CrossRefGoogle Scholar
  6. [Lam et al., 2010]
    Lam, H., Cowan, B., Nash, M., Young, A.: Interactive biventricular modeling tools for clinical cardiac image analysis. Journal of Cardiovascular Magnetic Resonance 12(suppl. 1), 248 (2010)CrossRefGoogle Scholar
  7. [Lötjönen et al., 2003]
    Lötjönen, J., Koikkalainen, J., Smutek, D., Kivistö, S., Lauerma, K.: Four-chamber 3-D statistical shape model from cardiac short-axis and long-axis MR images. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 459–466. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. [Mansi et al., 2009]
    Mansi, T., Durrleman, S., Bernhardt, B., Sermesant, M., Delingette, H., Voigt, I., Lurz, P., Taylor, A., Blanc, J., Boudjemline, Y., et al.: A Statistical Model of Right Ventricle in Tetralogy of Fallot for Prediction of Remodelling and Therapy Planning. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 214–221. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. [Nielsen, 1987]
    Nielsen, P.: The anatomy of the heart: a finite element model. PhD Thesis-University of Auckland (1987)Google Scholar
  10. [Ordas et al., 2007]
    Ordas, S., Oubel, E., Leta, R., Carreras, F., Frangi, A.: A statistical shape model of the heart and its application to model-based segmentation. In: Proc. SPIE Medical Imaging, San Diego, CA, USA, vol. 6511 (2007)Google Scholar
  11. [Papademetris et al., 2002]
    Papademetris, X., Sinusas, A., Dione, D., Constable, R., Duncan, J.: Estimation of 3-D left ventricular deformation from medical images using biomechanical models. IEEE Transactions on Medical Imaging 21(7), 786–800 (2002)CrossRefGoogle Scholar
  12. [Remme et al., 2004]
    Remme, E., Young, A., Augenstein, K., Cowan, B., Hunter, P.: Extraction and quantification of left ventricular deformation modes. IEEE Transactions on Biomedical Engineering 51(11), 1923–1931 (2004)CrossRefGoogle Scholar
  13. [Young et al., 2000]
    Young, A., Cowan, B., Thrupp, S., Hedley, W., Dell’Italia, L.: Left Ventricular Mass and Volume: Fast Calculation with Guide-Point Modeling on MR Images. Radiology 216(2), 597 (2000)Google Scholar
  14. [Young and Frangi, 2009]
    Young, A., Frangi, A.: Computational cardiac atlases: from patient to population and back. Experimental Physiology 94(5), 578 (2009)CrossRefGoogle Scholar
  15. [Zito et al., 2008]
    Zito, T., Wilbert, N., Wiskott, L., Berkes, P.: Modular toolkit for Data Processing (MDP): a Python data processing framework. Frontiers in neuroinformatics, 2 (2008)Google Scholar
  16. [Üzümcü et al., 2003]
    Üzümcü, M., Frangi, A., Sonka, M., Reiber, J., Lelieveldt, B.: ICA vs. PCA active appearance models: Application to cardiac MR segmentation. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 451–458. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pau Medrano-Gracia
    • 1
  • Brett R. Cowan
    • 1
  • J. Paul Finn
    • 2
  • Carissa G. Fonseca
    • 2
  • Alan H. Kadish
    • 3
  • Dan C. Lee
    • 3
  • Wenchao Tao
    • 2
  • Alistair A. Young
    • 1
  1. 1.Auckland Bioengineering InstituteUniversity of AucklandAucklandNew Zealand
  2. 2.Diagnostic CardioVascular Imaging, UCLALos AngelesUSA
  3. 3.Bluhm Cardiovascular Institute, Northwestern Memorial InstituteChicagoUSA

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