Personalization of cardiac fiber orientations from image data using the unscented kalman filter

Authors:
Andreas Nagler;Cristóbal Bertoglio;Michael Gee;Wolfgang Wall
Affiliations:
Institute for Computational Mechanics, Technische Universität München, Germany;Institute for Computational Mechanics, Technische Universität München, Germany;Mechanics & High Performance Computing Group, Technische Universität München, Germany;Institute for Computational Mechanics, Technische Universität München, Germany
Venue:
FIMH'13 Proceedings of the 7th international conference on Functional Imaging and Modeling of the Heart
Year:
2013

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Abstract

In this work, we propose to estimate rule-based myocardial fiber model (RBM) parameters from measured image data, with the goal of personalizing the fiber architecture for cardiac simulations. We first describe the RBM, which is based on a space-dependent angle distribution on the heart surface and then extended to the whole domain through an harmonic lifting of the fiber vectors. We then present a static Unscented Kalman Filter which we use for estimating the degrees of freedom of the fiber model. We illustrate the methodology using noisy synthetic data on a real heart geometry, as well as real DT-MRI-derived fiber data. We also show the impact of different fiber distributions on cardiac contraction simulations.