Abstract
Patient-specific models of the heart may lead to better understanding of cardiovascular diseases and better planning of therapy. A machine-learning approach to the personalization of an electro-mechanical model of the heart, from the kinematics of the endo- and epicardium, is presented in this paper. We use 4D mathematical currents to encapsulate information about the shape and deformation of the heart. The method is largely insensitive to initialization and does not require on-line simulation of the cardiac function. In this work, we demonstrate the performance of our approach for the joint estimation of three parameters on one heart geometry. We manage to retrieve parameters such that the model matches the 4D observations with an accuracy below the voxel size, in less than three minutes of computation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aronszajn, N.: Theory of reproducing kernels. Harvard University (1951)
Bestel, J., Clément, F., Sorine, M.: A Biomechanical Model of Muscle Contraction. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 1159–1161. Springer, Heidelberg (2001)
Chabiniok, R., Moireau, P., Lesault, P.F., Rahmouni, A., Deux, J.F., Chapelle, D.: Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model. Biomechanics and Modeling in Mechanobiology 11(5), 609–630 (2012)
Chapelle, D., Le Tallec, P., Moireau, P., Sorine, M.: An energy-preserving muscle tissue model: formulation and compatible discretizations. IJMCE 10(2), 189–211 (2012)
Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximations. Constructive Approximation 13(1), 57–98 (1997)
Delingette, H., Billet, F., Wong, K., Sermesant, M., Rhode, K., Ginks, M., Rinaldi, C., Razavi, R., Ayache, N., et al.: Personalization of Cardiac Motion and Contractility from Images using Variational Data Assimilation. IEEE Trans. Biomed. Eng. 59(1), 20 (2012)
Durrleman, S.: Statistical models of currents for measuring the variability of anatomical curves, surfaces and their evolution. Ph.D. Thesis, INRIA (March 2010)
Durrleman, S., Pennec, X., Trouvé, A., Ayache, N.: Measuring Brain Variability Via Sulcal Lines Registration: A Diffeomorphic Approach. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 675–682. Springer, Heidelberg (2007)
Durrleman, S., Pennec, X., Trouvé, A., Ayache, N.: Sparse Approximation of Currents for Statistics on Curves and Surfaces. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 390–398. Springer, Heidelberg (2008)
Gärtner, T., Flach, P., Kowalczyk, A., Smola, A.: Multi-instance kernels. In: Proceedings of the 19th International Conference on Machine Learning, pp. 179–186 (2002)
Haussler, D.: Convolution kernels on discrete structures. Tech. rep., Technical report, UC Santa Cruz (1999)
Hoerl, A., Kennard, R.: Ridge regression: Biased estimation for nonorthogonal problems. Technometrics pp. 55–67 (1970)
Imperiale, A., Chabiniok, R., Moireau, P., Chapelle, D.: Constitutive Parameter Estimation Methodology Using Tagged-MRI Data. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 409–417. Springer, Heidelberg (2011)
Liu, H., Shi, P.: Maximum a posteriori strategy for the simultaneous motion and material property estimation of the heart. IEEE Trans. Biomed. Eng. 56(2), 378–389 (2009)
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 92(1), 92–111 (2011)
Marchesseau, S., Delingette, H., Sermesant, M., Rhode, K., Duckett, S.G., Rinaldi, C.A., Razavi, R., Ayache, N.: Cardiac Mechanical Parameter Calibration Based on the Unscented Transform. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 41–48. Springer, Heidelberg (2012)
Schölkopf, B., Smola, A.: Learning with kernels: Support vector machines, regularization, optimization, and beyond. The MIT Press (2002)
Vaillant, M., Glaunès, J.: Surface Matching via Currents. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 381–392. Springer, Heidelberg (2005)
Xiang, Y., Gubian, S., Suomela, B., Hoeng, J.: Generalized simulated annealing for efficient global optimization: the GenSA package for R. The R Journal (2012) (forthcoming)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Le Folgoc, L., Delingette, H., Criminisi, A., Ayache, N. (2013). Current-Based 4D Shape Analysis for the Mechanical Personalization of Heart Models. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2012. Lecture Notes in Computer Science, vol 7766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36620-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-36620-8_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36619-2
Online ISBN: 978-3-642-36620-8
eBook Packages: Computer ScienceComputer Science (R0)