Constitutive Parameter Estimation Methodology Using Tagged-MRI Data

  • A. Imperiale
  • R. Chabiniok
  • P. Moireau
  • D. Chapelle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6666)


We propose a methodology for performing the estimation of a key constitutive parameter in a biomechanical heart model – namely, the tissue contractility – using tagged-MRI data. We adopt a sequential data assimilation strategy, and the image data is assumed to be processed in the form of deforming tag planes, which we employ to obtain a discrepancy between the model and the data by computing distances to these surfaces. We assess our procedure using synthetic measurements produced with a model representing an infarcted heart as observed in an animal experiment, and the estimation results are found to be of superior accuracy compared to assimilation based on segmented endo- and epicardium surfaces.


Data Assimilation Infarcted Heart Synthetic Measurement Tissue Contractility Synthetic Observation 
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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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • A. Imperiale
    • 1
  • R. Chabiniok
    • 1
  • P. Moireau
    • 1
  • D. Chapelle
    • 1
  1. 1.INRIAMACS TeamLe ChesnayFrance

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