Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model

R. Chabiniok, P. Moireau, P. F. Lesault, A. Rahmouni, J. F. Deux, D. Chapelle

Research output: Contribution to journalArticlepeer-review

84 Scopus citations


The objective of this paper is to propose and assess an estimation procedure-based on data assimilation principles-well suited to obtain some regional values of key biophysical parameters in a beating heartmodel, using actual Cine-MR images. The motivation is twofold: (1) to provide an automatic tool for personalizing the characteristics of a cardiac model in order to achieve predictivity in patient-specific modeling and (2) to obtain some useful information for diagnosis purposes in the estimated quantities themselves. In order to assess the global methodology, we specifically devised an animal experiment in which a controlled infarct was produced and data acquired before and after infarction, with an estimation of regional tissue contractility-a key parameter directly affected by the pathology-performed for everymeasured stage. After performing a preliminary assessment of our proposed methodology using synthetic data, we then demonstrate a full-scale application by first estimating contractility values associated with 6 regions based on the AHA subdivision, before running a more detailed estimation using the actual AHA segments. The estimation results are assessed by comparison with the medical knowledge of the specific infarct, and with late enhancement MR images. We discuss their accuracy at the various subdivision levels, in the light of the inherent modeling limitations and of the intrinsic information contents featured in the data.

Original languageEnglish (US)
Pages (from-to)609-630
Number of pages22
JournalBiomechanics and Modeling in Mechanobiology
Issue number5
StatePublished - May 2012
Externally publishedYes


  • Clinical data
  • Data assimilation
  • Filtering
  • Patient-specific cardiac modeling
  • State and parameter estimation

ASJC Scopus subject areas

  • Biotechnology
  • Modeling and Simulation
  • Mechanical Engineering


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