Abstract
Aortic valve disease is one of the most common heart valve diseases. Among various approaches treating this valve pathology we address aortic valve reconstruction with glutaraldehyde-treated autologous pericardium. This procedure is attractive due to its cost and effectiveness. A surgical planning system based on patient-specific modeling allows surgeons to compare different shapes of valve leaflet and to choose optimal reconstruction strategies. We develop a numerical framework for assessment of valve function that can be utilized by surgeons during patient-specific decision making. The framework includes automatic CT image segmentation, mesh generation, simulation of valve leaflet deformation by mass-spring approach. The final decision is based on uncertainty analysis and leaflets shape optimization. This paper gives a proof of concept of our methodology: segmentation, meshing and deformation simulation methods are presented in details.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Coffey, S., Cairns, B.J., Iung, B.: The modern epidemiology of heart valve disease. Heart 102(1), 75–85 (2016). https://doi.org/10.1136/heartjnl-2014-307020
Rankin, J.S., Nöbauer, C., Crooke, P.S., Schreiber, C., Lange, R., Mazzitelli, D.: Techniques of autologous pericardial leaflet replacement for aortic valve reconstruction. Ann. Thorac. Surg. 98(2), 743–745 (2014). https://doi.org/10.1016/j.athoracsur.2013.11.086
Hammer, P.E., Sacks, M.S., Pedro, J., Howe, R.D.: Mass-spring model for simulation of heart valve tissue mechanical behavior. Ann. Biomed. Eng. 39(6), 1668–1679 (2011). https://doi.org/10.1007/s10439-011-0278-5
Hammer, P.E., Pedro, J., Howe, R.D.: Anisotropic mass-spring method accurately simulates mitral valve closure from image-based models. In: International Conference on Functional Imaging and Modeling of the Heart, pp. 233–240. Springer, Berlin (2011)
Pappalardo, O.A., Sturla, F., Onorati, F., Puppini, G., Selmi, M., Luciani, G.B., Faggian, G., Redaelli, A., Votta, E.: Mass-spring models for the simulation of mitral valve function: looking for a trade-off between reliability and time-efficiency. Med. Eng. Phys. 47, 93–104 (2017). https://doi.org/10.1016/j.medengphy.2017.07.001
Danilov, A., Ivanov, Yu., Pryamonosov, R., Vassilevski, Yu.: Methods of graph network reconstruction in personalized medicine. Int. J. Numer. Methods Biomed. Eng. 32 (2016). https://doi.org/10.1002/cnm.2754
Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972) https://doi.org/10.1145/361237.361242
Grady, L.: Fast, quality, segmentation of large volumes – isoperimetric distance trees. In: Computer Vision — ECCV (2006). https://doi.org/10.1007/11744078_35
Yushkevich, P.A., Piven, J., Cody Hazlett, H., Gimpel Smith, R., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–28 (2006). https://doi.org/10.1016/j.neuroimage.2006.01.015
Rineau, L., Yvinec, M.: A generic software design for Delaunay refinement meshing. Comput. Geom. 38, 100–110 (2007). https://doi.org/10.1016/j.comgeo.2006.11.008
Lipnikov, K., Vassilevski, Yu., Danilov, A., et al.: Advanced numerical instruments 2D. https://sourceforge.net/projects/ani2d
Zigras, T.C.: Biomechanics of human pericardium: a comparative study of fresh and fixed tissue, Doctoral dissertation, McGill University (2007)
Hammer, P.E., Perrin, D.P., Pedro, J., Howe, R.D.: Image-based mass-spring model of mitral valve closure for surgical planning. In: Proceedings of SPIE Medical Imaging: Visualization, Image-Guided Procedures, and Modeling, vol. 6918, p. 69180Q (2008)
Acknowledgements
The authors thank P. A. Karavaykin for problem formulation and valuable discussions, Ph. Yu. Kopylov for providing patient-specific data, and G. V. Kopytov for the development of user’s interface for our methodology. The work was supported by the Russian Foundation for Basic Research (RFBR) under grant 17-01-00886.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Danilov, A., Liogky, A., Pryamonosov, R., Salamatova, V. (2019). Patient-Specific Geometric Modeling of an Aortic Valve. In: Garanzha, V., Kamenski, L., Si, H. (eds) Numerical Geometry, Grid Generation and Scientific Computing. Lecture Notes in Computational Science and Engineering, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-030-23436-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-23436-2_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23435-5
Online ISBN: 978-3-030-23436-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)