Paper Info
Title
Manifold Learning For Cardiac Modeling And Estimation Framework
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.
Year
DOI
Venue
2014
10.1007/978-3-319-14678-2_30
STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART: IMAGING AND MODELLING CHALLENGES
Field
DocType
Volume
Pattern recognition,Computer science,Algorithm,Kalman filter,Synthetic data,Ground truth,Contractility,Artificial intelligence,Parameter space,Initialization,Nonlinear dimensionality reduction,Estimator
Conference
8896
ISSN
Citations 
PageRank 
0302-9743
1
0.39
References 
Authors
14
5
Name
Order
Citations
PageRank
Radomir Chabiniok1779.71
Kanwal K Bhatia219014.78
Andrew P. King348359.98
Daniel Rueckert49338637.58
N P Smith57910.92