Home Publications Research Grants Inventions & Patents Awards Additional Research Activities Faculties & Institutions Research Areas

Manifold learning for cardiac modeling and estimation framework

Chabiniok R, Bhatia KK, King AP, Rueckert D, Smith N (2015)

---

Publication Type: Conference contribution

Publication year: 2015

Journal

Publisher: Springer Verlag

Book Volume: 8896

Pages Range: 284-294

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Boston, MA, USA

ISBN: 9783319146775

DOI: 10.1007/978-3-319-14678-2_30

Abstract

In this work we apply manifold learning to biophysical modeling of cardiac contraction with the aim of estimating material parameters characterizing myocardial stiffness and contractility. The set of cardiac cycle simulations spanning the parameter space of myocardial stiffness and contractility is used to create a manifold structure based on the motion pattern of the left ventricle endocardial surfaces. First, we assess the proposed method by using synthetic data generated by the model specifically to test our approach with the known ground truth parameter values. Then, we apply the method on cardiac magnetic resonance imaging (MRI) data of two healthy volunteers. The post-processed cine MRI for each volunteer were embedded into the manifold together with the simulated samples and the global parameters of contractility and stiffness for the whole myocardium were estimated. Then, we used these parameters as an initialization into an estimator of regional contractilities based on a reduced order unscented Kalman filter. The global values of stiffness and contractility obtained by manifold learning corrected the model in comparison to a standard model calibration by generic parameters, and a significantly more accurate estimation of regional contractilities was reached when using the initialization given by manifold learning.

Involved external institutions

How to cite

APA:

Chabiniok, R., Bhatia, K.K., King, A.P., Rueckert, D., & Smith, N. (2015). Manifold learning for cardiac modeling and estimation framework. In Mihaela Pop, Tommaso Mansi, Oscar Camara, Maxime Sermesant, Alistair Young, Kawal Rhode, Kawal Rhode (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 284-294). Boston, MA, USA: Springer Verlag.

MLA:

Chabiniok, Radomir, et al. "Manifold learning for cardiac modeling and estimation framework." Proceedings of the 5th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2014 Held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2014, Boston, MA, USA Ed. Mihaela Pop, Tommaso Mansi, Oscar Camara, Maxime Sermesant, Alistair Young, Kawal Rhode, Kawal Rhode, Springer Verlag, 2015. 284-294.

BibTeX: Download