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Continuous Spatio-temporal Atlases of the Asymptomatic and Infarcted Hearts

  • Pau Medrano-Gracia
  • Brett R. Cowan
  • David A. Bluemke
  • J. Paul Finn
  • Alan H. Kadish
  • Daniel C. Lee
  • João A. C. Lima
  • Avan Suinesiaputra
  • Alistair A. Young
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8330)

Abstract

Statistical descriptions of regional wall motion abnormalities of the heart are key to understanding both sub-clinical and clinical progression of dysfunction. In this paper we establish a temporal registration framework of the cardiac cycle to build a spatio-temporal atlas of 300 asymptomatic volunteers and 300 symptomatic patients with myocardial infarction. A finite-element model was customised to each person’s magnetic resonance images with expert-guided semi-automatic spatial and temporal registration of model parameters. A piece-wise linear temporal registration from user-defined key frames was followed by a Fourier series temporal estimation, providing temporal continuity. All spatial and temporal data were then statistically analysed by means of principal component analysis. Results show differences in sphericity, wall thickening and mitral valve dynamics between the two groups. The modes are available from www.cardiacatlas.org . These atlases can be readily applied to abnormality detection and quantification and can also aid in anatomically constrained shape-based algorithms in automatic planning or segmentation.

Keywords

Regional Wall Motion Regional Wall Motion Abnormality Statistical Shape Model Nonrigid Image Registration Leave Ventricular Model 
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.

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References

  1. 1.
    Bild, D., Bluemke, D., Burke, G., Detrano, R., Diez Roux, A., Folsom, A., Greenland, P., et al.: Multi-ethnic study of atherosclerosis: objectives and design. American Journal of Epidemiology 156(9), 871 (2002)CrossRefGoogle Scholar
  2. 2.
    Brown, J., Churchill, R.: Fourier series and boundary value problems. Recherche 67, 2 (1993)Google Scholar
  3. 3.
    Chandrashekara, R., Rao, A., Sanchez-Ortiz, G.I., Mohiaddin, R.H., Rueckert, D.: Construction of a statistical model for cardiac motion analysis using nonrigid image registration. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 599–610. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Dierckx, P.: Curve and surface fitting with splines. Oxford University Press, USA (1995)Google Scholar
  5. 5.
    Duchateau, N., De Craene, M., Piella, G., Silva, E., Doltra, A., Sitges, M., Bijnens, B., Frangi, A.: A spatiotemporal statistical atlas of motion for the quantification of abnormal myocardial tissue velocities. Medical Image Analysis 15(3), 316–328 (2011)CrossRefGoogle Scholar
  6. 6.
    Fonseca, C., Backhaus, M., Bluemke, D., Britten, R., Do Chung, J., Cowan, B., Dinov, I., Finn, J., Hunter, P., Kadish, A., et al.: The Cardiac Atlas Project – an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics (2011)Google Scholar
  7. 7.
    Guyton, A., Hall, J.: Medical Physiology. Saunders, Philadelphia (2000)Google Scholar
  8. 8.
    Hoogendoorn, C., Duchateau, N., Sánchez-Quintana, D., Whitmarsh, T., Sukno, F., De Craene, M., Lekadir, K., Frangi, A.: A high-resolution atlas and statistical model of the human heart from multislice CT. IEEE Transactions on Medical Imaging (2013)Google Scholar
  9. 9.
    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
  10. 10.
    Kaus, M.R., von Berg, J., Niessen, W.J., Pekar, V.: Automated segmentation of the left ventricle in cardiac MRI. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 432–439. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Lewandowski, A.J., Augustine, D., Lamata, P., Davis, E.F., Lazdam, M., Francis, J., McCormick, K., Wilkinson, A., Singhal, A., Lucas, A., et al.: The preterm heart in adult life: Cardiovascular magnetic resonance reveals distinct differences in left ventricular mass, geometry and function. Circulation (2012)Google Scholar
  12. 12.
    Lötjönen, J., Kivistö, S., Koikkalainen, J., Smutek, D., Lauerma, K.: Statistical shape model of atria, ventricles and epicardium from short-and long-axis MR images. Medical Image Analysis 8(3), 371–386 (2004)CrossRefGoogle Scholar
  13. 13.
    Medrano-Gracia, P., Cowan, B., Finn, J., Fonseca, C., Kadish, A., Lee, D., Tao, W., Young, A.: The cardiac atlas project: preliminary description of heart shape in patients with myocardial infarction. Statistical Atlases and Computational Models of the Heart, 46–53 (2010)Google Scholar
  14. 14.
    Medrano-Gracia, P., Cowan, B.R., Bluemke, D.A., Finn, J.P., Lima, J.A.C., Suinesiaputra, A., Young, A.A.: Large scale left ventricular shape atlas using automated model fitting to contours. In: Ourselin, S., Rueckert, D., Smith, N. (eds.) FIMH 2013. LNCS, vol. 7945, pp. 433–441. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Mei, L., Figl, M., Darzi, A., Rueckert, D., Edwards, P.: Sample sufficiency and PCA dimension for statistical shape models. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 492–503. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Nielsen, P., Le Grice, I., Smaill, B., Hunter, P.: Mathematical model of geometry and fibrous structure of the heart. American Journal of Physiology- Heart and Circulatory Physiology 260(4), H1365 (1991)Google Scholar
  17. 17.
    Piessens, R., Doncker-Kapenga, D., Überhuber, C., Kahaner, D., et al.: QUADPACK, a subroutine package for automatic integration. Springer (1983)Google Scholar
  18. 18.
    Ramsay, J.: Functional data analysis. Wiley Online Library (2006)Google Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    Young, A.A., Hunter, P.J., Smaill, B.H.: Estimation of epicardial strain using the motions of coronary bifurcations in biplane cineangiography. IEEE Transactions on Biomedical Engineering 39(5), 526–531 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Pau Medrano-Gracia
    • 1
  • Brett R. Cowan
    • 1
  • David A. Bluemke
    • 2
  • J. Paul Finn
    • 3
  • Alan H. Kadish
    • 4
  • Daniel C. Lee
    • 4
  • João A. C. Lima
    • 5
  • Avan Suinesiaputra
    • 1
  • Alistair A. Young
    • 1
  1. 1.Dept. Anatomy with RadiologyUniversity of AucklandNew Zealand
  2. 2.NIH Clinical Ctr.BethesdaUSA
  3. 3.Diagnostic CardioVascular Imaging SectionUCLALos AngelesUSA
  4. 4.Feinberg Cardiovascular Research Inst.Northwestern UniversityChicagoUSA
  5. 5.The Johns Hopkins HospitalBaltimoreUSA

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