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International Conference on Functional Imaging and Modeling of the Heart

FIMH 2013: Functional Imaging and Modeling of the Heart pp 132–140Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNIP,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.

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Author information

Authors and Affiliations

  1. Institute for Computational Mechanics, Technische Universität München, Germany

    Andreas Nagler, Cristóbal Bertoglio & Wolfgang Wall

  2. Mechanics & High Performance Computing Group, Technische Universität München, Germany

    Michael Gee

Authors
  1. Andreas Nagler
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  2. Cristóbal Bertoglio
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  3. Michael Gee
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  4. Wolfgang Wall
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Editor information

Editors and Affiliations

  1. Centre for Medical Image Computing, University College London, Front Engineering Building, Malet Place, WC1E 6BT, London, UK

    Sébastien Ourselin

  2. Department of Computing, Imperial College London, 180 Queen’s Gate, SW7 2AZ, London, UK

    Daniel Rueckert

  3. Imaging Sciences & Biomedical Division, St. Thomas Hospital, King’s College London, St. Thomas Hospital, SE1 7EH, London, UK

    Nicolas Smith

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© 2013 Springer-Verlag Berlin Heidelberg

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Nagler, A., Bertoglio, C., Gee, M., Wall, W. (2013). Personalization of Cardiac Fiber Orientations from Image Data Using the Unscented Kalman Filter. In: Ourselin, S., Rueckert, D., Smith, N. (eds) Functional Imaging and Modeling of the Heart. FIMH 2013. Lecture Notes in Computer Science, vol 7945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38899-6_16

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  • DOI: https://doi.org/10.1007/978-3-642-38899-6_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38898-9

  • Online ISBN: 978-3-642-38899-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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