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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 41–48Cite as

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Cardiac Mechanical Parameter Calibration Based on the Unscented Transform

Cardiac Mechanical Parameter Calibration Based on the Unscented Transform

  • Stéphanie Marchesseau19,
  • Hervé Delingette19,
  • Maxime Sermesant19,
  • Kawal Rhode20,
  • Simon G. Duckett20,
  • C. Aldo Rinaldi20,
  • Reza Razavi20 &
  • …
  • Nicholas Ayache19 
  • Conference paper
  • 4004 Accesses

  • 4 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7511)

Abstract

Patient-specific cardiac modelling can help in understanding pathophysiology and predict therapy planning. However it requires to personalize the model geometry, kinematics, electrophysiology and mechanics. Calibration aims at providing global values (space invariant) of parameters before performing the personalization stage which involves solving an inverse problem to find regional values. We propose an automatic calibration method of the mechanical parameters of the Bestel-Clément-Sorine (BCS) electromechanical model of the heart based on the Unscented Transform algorithm. A sensitivity analysis is performed that reveals which observations on the volume and pressure evolution are significant to characterize the global behaviour of the myocardium. We show that the calibration method gives satisfying results by optimizing up to 7 parameters of the BCS model in only one iteration. This method was evaluated on 7 volunteers and 2 heart failure patients, with a mean relative error from the real data of 11%. This calibration enabled furthermore a preliminary study of the specific parameters to the studied pathologies.

Keywords

  • Calibration Method
  • Volume Curve
  • Valve Model
  • Unscented Transform
  • Electromechanical Model

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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References

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Author information

Authors and Affiliations

  1. Asclepios Research Project, INRIA Sophia Antipolis, France

    Stéphanie Marchesseau, Hervé Delingette, Maxime Sermesant & Nicholas Ayache

  2. Division of Imaging Sciences & Biomedical Engineering, King’s College London, St. Thomas’ Hospital, London, UK

    Kawal Rhode, Simon G. Duckett, C. Aldo Rinaldi & Reza Razavi

Authors
  1. Stéphanie Marchesseau
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  2. Hervé Delingette
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  3. Maxime Sermesant
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  4. Kawal Rhode
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  5. Simon G. Duckett
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  6. C. Aldo Rinaldi
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  7. Reza Razavi
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  8. Nicholas Ayache
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Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Marchesseau, S. et al. (2012). Cardiac Mechanical Parameter Calibration Based on the Unscented Transform. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-33418-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33417-7

  • Online ISBN: 978-3-642-33418-4

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