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Personalization of Cardiac Fiber Orientations from Image Data Using the Unscented Kalman Filter

  • Andreas Nagler
  • Cristóbal Bertoglio
  • Michael Gee
  • Wolfgang Wall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7945)

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.

Keywords

Unscented Kalman Filter Fiber Distribution Rule Base Model Fiber Model Contraction Pattern 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andreas Nagler
    • 1
  • Cristóbal Bertoglio
    • 1
  • Michael Gee
    • 2
  • Wolfgang Wall
    • 1
  1. 1.Institute for Computational MechanicsTechnische Universität MünchenGermany
  2. 2.Mechanics & High Performance Computing GroupTechnische Universität MünchenGermany

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