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Atlas Construction and Image Analysis Using Statistical Cardiac Models

  • Conference paper
Statistical Atlases and Computational Models of the Heart (STACOM 2010)

Abstract

This paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Application-wise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations.

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References

  1. Alexander, D., et al.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans. Med. Imag. 20(11), 1131–1139 (2001)

    Article  Google Scholar 

  2. Anderson, R.H., et al.: The three-dimensional arrangement of the myocytes in the ventricular walls. Clin. Anat. 22, 64–76 (2009)

    Article  Google Scholar 

  3. Ardekani, S., et al.: Computational method for identifying and quantifying shape features of human left ventricular remodeling. Ann. Biomed. Eng. 37(6), 1043–1054 (2009)

    Article  Google Scholar 

  4. Ashburner, J., Friston, K.J.: Voxel-based morphometry – the methods. NeuroImage 11(6), 805–821 (2000)

    Article  Google Scholar 

  5. Basser, P.J., et al.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. Ser. B 103(3), 247–254 (1994)

    Article  Google Scholar 

  6. Blezek, D.J., Miller, J.V.: Atlas stratification. Med. Image Anal. 11(5), 443–457 (2007)

    Article  Google Scholar 

  7. Chandrashekara, R., et al.: Analysis of 3-D myocardial motion in tagged MR images using nonrigid image registration. IEEE Trans. Med. Imag. 23(10), 1245–1250 (2004)

    Article  Google Scholar 

  8. Commowick, O., et al.: Detection of DTI white matter abnormalities in multiple sclerosis patients. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 975–982. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Cootes, T.F., Taylor, C.J.: Anatomical statistical models and their role infeature extraction. Brit. J. Radiol. 77, S133–S139 (2004)

    Article  Google Scholar 

  10. Cootes, T.F., Taylor, C.J.: Statistical models of appearance for computer vision. Tech. Rep., University of Manchester, UK (2004)

    Google Scholar 

  11. De Craene, M., et al.: Temporal diffeomorphic free-form deformation for strain quantification in 3D-US images. In: MICCAI 2010 (2010) (in press)

    Google Scholar 

  12. De Craene, M., et al.: Large diffeomorphic FFD registration for motion and strain quantification from 3D-US sequences. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 437–446. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  13. De Lathauwer, L., et al.: A multilinear singular value decomposition. SIAM J. Matrix Anal. A. 21(4), 1253–1278 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  14. Diedrichsen, J.: A spatially unbiased atlas template of the human cerebellum. NeuroImage 33(1), 127–138 (2006)

    Article  Google Scholar 

  15. Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. Wiley, Chichester (1998)

    MATH  Google Scholar 

  16. Duchateau, N., et al.: Septal flash assessment on CRT candidates based on statistical atlases of motion. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 759–766. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  17. Durrleman, S., et al.: Spatiotemporal atlas estimation for developmental delay detection in longitudinal datasets. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 297–304. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  18. Figueras i Ventura, R.M., et al.: Bilinear point distribution models for heart motion analysis. In: ISBI 2010, pp. 476–479 (2010)

    Google Scholar 

  19. Frangi, A.F., et al.: Automatic construction of multiple-object three-dimensional statistical shape models: Application to cardiac modeling. IEEE Trans. Med. Imag. 21(9), 1151–1166 (2002)

    Article  Google Scholar 

  20. Guimond, A., et al.: Average brain models: A convergence study. Comput. Vision and Image Understanding 77(2), 192–210 (1999)

    Article  Google Scholar 

  21. Hansegård, J., et al.: Constrained active appearance models for segmentation of triplane echocardiograms. IEEE Trans. Med. Imag. 26(10), 1391–1400 (2007)

    Article  Google Scholar 

  22. Hoogendoorn, C., et al.: Bilinear models for spatio-temporal point distribution analysis: Application to extrapolation of left ventricular, biventricular and whole heart cardiac dynamics. Int. J. Comput. Vision (2009) (in Press)

    Google Scholar 

  23. Hoogendoorn, C., et al.: A groupwise mutual information metric for cost efficient selection of a suitable reference in cardiac computational atlas construction. In: SPIE Med. Im., vol. 7623, pp. 76231R–76231R-9 (2010)

    Google Scholar 

  24. Jensen, J.A., Svendsen, N.B.: Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers. IEEE T. Ultrason. Ferr. 39(2), 262–267 (1992)

    Article  Google Scholar 

  25. Kwan, R.K., et al.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans. Med. Imag. 18(11), 1085–1097 (1999)

    Article  Google Scholar 

  26. Kwon, D.H., et al.: Steep left ventricle to aortic root angle and hypertrophic obstructive cardiomyopathy: study of a novel association using three-dimensional multimodality imaging. Heart 95(21), 1784–1791 (2009)

    Article  Google Scholar 

  27. Ledesma-Carbayo, M.J., et al.: Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation. IEEE Trans. Med. Imag. 24(9), 1113–1126 (2005)

    Article  Google Scholar 

  28. Miller, M.I., Qiu, A.: The emerging discipline of computational functional anatomy. NeuroImage 45(1), suppl. 1, 16–39 (2009)

    Article  Google Scholar 

  29. Mori, S., van Zijl, P.C.M.: Fiber tracking: principles and strategies - a technical review. NMR Biomed. 15, 468–480 (2002)

    Article  Google Scholar 

  30. Muñoz Moreno, E., Frangi, A.F.: Spatial normalization of cardiac diffusion tensor imaging for modeling the muscular structure of the myocardium. In: ICIP 2010 (2010) (in press)

    Google Scholar 

  31. Ordás, S., et al.: A statistical shape model of the heart and its application to model-based segmentation. In: SPIE Med. Im., vol. 6511, p. 6511K (2007)

    Google Scholar 

  32. Park, H., et al.: Least biased target selection in probabilistic atlas construction. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 419–426. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  33. Park, H., et al.: Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans. Med. Imag. 22(4), 483–492 (2003)

    Article  Google Scholar 

  34. Parsai, C., et al.: Toward understanding response to cardiac resynchronization therapy: left ventricular dyssynchrony is only one of multiple mechanisms. Eur. Heart J. 30(8), 940–949 (2009)

    Article  Google Scholar 

  35. Perperidis, D., et al.: Spatio-temporal free-form registration of cardiac MR image sequences. Med. Image Anal. 9(5), 441–456 (2005)

    Article  Google Scholar 

  36. Peters, J., et al.: Optimizing boundary detection via simulated search with applications to multi-modal heart segmentation. Med. Image Anal. 14, 70–84 (2010)

    Article  Google Scholar 

  37. Petersen, S.E., et al.: Differentiation of athlete’s heart from pathological forms of cardiac hypertrophy by means of geometric indices derived from cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 7(3), 551–558 (2005)

    Article  Google Scholar 

  38. Peyrat, J.-M., et al.: Registration of 4D cardiac CT sequences under trajectory constraints with multichannel diffeomorphic demons. IEEE Trans. Med. Imag. 29(7), 1351–1368 (2010)

    Article  Google Scholar 

  39. Qiu, A., et al.: Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes. NeuroImage 45(1), supp. 1, 51–60 (2009)

    Article  Google Scholar 

  40. Rao, A., et al.: Spatial transformation of motion and deformation fields using nonrigid registration. IEEE Trans. Med. Imag. 23(9), 1065–1076 (2004)

    Article  Google Scholar 

  41. Remme, E.W., et al.: Extraction and quantification of left ventricular deformation modes. IEEE Trans. Biomed. Eng. 51(11), 1923–1931 (2004)

    Article  Google Scholar 

  42. Rueckert, D., et al.: Diffeomorphic registration using B-splines. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 702–709. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  43. Segars, W.P., et al.: Realistic CT simulation using the 4D XCAT phantom. Med. Phys. 35(8), 3800–3808 (2008)

    Article  Google Scholar 

  44. Sjöstrand, K., et al.: Sparse decomposition and modeling of anatomical shape variation. IEEE Trans. Med. Imag. 26(12), 1625–1635 (2007)

    Article  Google Scholar 

  45. Suinesiaputra, A., et al.: Automated detection of regional wall motion abnormalities based on a statistical model applied to multi-slice short-axis cardiac MR images. IEEE Trans. Med. Imag. 28(4), 595–607 (2009)

    Article  Google Scholar 

  46. Tenenbaum, J.B., Freeman, W.T.: Separating style and content with bilinear models. Neural Comput. 12(6), 1247–1283 (2000)

    Article  Google Scholar 

  47. Tobon-Gomez, C., et al.: Automatic construction of 3D-ASM intensity models by simulating image acquisition: Application to myocardial gated SPECT studies. IEEE Trans. Med. Imag. 27(11), 1655–1667 (2008)

    Article  Google Scholar 

  48. Tobon-Gomez, C., et al.: 3D mesh based wall thickness measurement: identification of left ventricular hypertrophy phenotypes. In: EMBS 2010 (2010) (in press)

    Google Scholar 

  49. Trouvé, A.: Diffeomorphisms groups and pattern matching in image analysis. Int. J. Comput. Vision 28(3), 213–221 (1998)

    Article  Google Scholar 

  50. Twining, C.J., Marsland, S.: Constructing an atlas for the diffeomorphism group of a compact manifold with boundary, with application to the analysis of image registrations. J. Comput. Appl. Math. 222(2), 411–428 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  51. Vercauteren, T., et al.: Non-parametric diffeomorphic image registration with the demons algorithm. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 319–326. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  52. Young, A.A., Frangi, A.F.: Computational cardiac atlases: from patient to population and back. Experimental Physiology 94(5), 578–596 (2009)

    Article  Google Scholar 

  53. Zhu, Y., et al.: Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model. IEEE Trans. Med. Imag. 29(3), 669–687 (2010)

    Article  Google Scholar 

  54. Zimmerman, V., et al.: Modeling the Purkinje conduction system with a non deterministic rule based iterative method. IEEE Computers in Cardiology 36, 461–464 (2009)

    Google Scholar 

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De Craene, M. et al. (2010). Atlas Construction and Image Analysis Using Statistical Cardiac Models. In: Camara, O., Pop, M., Rhode, K., Sermesant, M., Smith, N., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. STACOM 2010. Lecture Notes in Computer Science, vol 6364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15835-3_1

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  • DOI: https://doi.org/10.1007/978-3-642-15835-3_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15834-6

  • Online ISBN: 978-3-642-15835-3

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