Abstract
Patients with repaired Tetralogy of Fallot commonly suffer from chronic pulmonary valve regurgitations and extremely dilated right ventricle (RV). To reduce risk factors, new pulmonary valves must be re-implanted. However, establishing the best timing for re-intervention is a clinical challenge because of the large variability in RV shape and in pathology evolution. This study aims at quantifying the regional impacts of growth and regurgitations upon the end-diastolic RV anatomy. The ultimate goal is to determine, among clinical variables, predictors for the shape in order to build a statistical model that predicts RV remodelling. The proposed approach relies on a forward model based on currents and LDDMM algorithm to estimate an unbiased template of 18 patients and the deformations towards each individual shape. Cross-sectional multivariate analyses are carried out to assess the effects of body surface area, tricuspid and transpulmonary valve regurgitations upon the RV shape. The statistically significant deformation modes were found clinically relevant. Canonical correlation analysis yielded a generative model that was successfully tested on two new patients.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Geva, T.: Indications and timing of pulmonary valve replacement after tetralogy of Fallot repair. In: Seminars in Thoracic and Cardiovascular Surgery: Pediatric Cardiac Surgery Annual, vol. 9, pp. 11–22. Elsevier, Amsterdam (2006)
Sheehan, F., Ge, S., Vick III, G., Urnes, K., Kerwin, W., Bolson, E., Chung, T., Kovalchin, J., Sahn, D., Jerosch-Herold, M., et al.: Three-Dimensional Shape Analysis of Right Ventricular Remodeling in Repaired Tetralogy of Fallot. The American Journal of Cardiology 101(1), 107 (2008)
Zhang, H., Walker, N., Mitchell, S., Thomas, M., Wahle, A., Scholz, T., Sonka, M.: Analysis of four-dimensional cardiac ventricular magnetic resonance images using statistical models of ventricular shape and cardiac motion. In: Proc. SPIE 2006, vol. 6143, p. 614307 (2006)
Guimond, A., Meunier, J., Thirion, J.P.: Average brain models: A convergence study. Computer Vision and Image Understanding 77(2), 192–210 (2000)
Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, 151–160 (2004)
Allassonniere, S., Amit, Y., Trouve, A.: Towards a coherent statistical framework for dense deformable template estimation. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69(1), 3–29 (2007)
Durrleman, S., Pennec, X., Trouvé, A., Ayache, N.: A forward model to build unbiased atlases from curves and surfaces. In: Pennec, X., Joshi, S. (eds.) Proc. of the International Workshop on the Mathematical Foundations of Computational Anatomy, MFCA 2008 (2008)
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)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Fast automatic heart chamber segmentation from 3D CT data using marginal space learning and steerable features. In: Proc. ICCV 2007, pp. 1–8 (2007)
Jian, B., Vemuri, B.: A robust algorithm for point set registration using mixture of Gaussians. In: Proc. ICCV 2005, vol. 2, pp. 1246–1251 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Electronic Supplementary Material
Supplementary material (930 KB)
Supplementary material(2,258 KB)
Supplementary material(1,848 KB)
Supplementary material(2,549 KB)
Supplementary material(2,338 KB)
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mansi, T. et al. (2009). A Statistical Model of Right Ventricle in Tetralogy of Fallot for Prediction of Remodelling and Therapy Planning. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-04268-3_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04267-6
Online ISBN: 978-3-642-04268-3
eBook Packages: Computer ScienceComputer Science (R0)