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

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• 作者：R. Chabiniok (1)
  P. Moireau (1)
  P.-F. Lesault (2)
  A. Rahmouni (2)
  J.-F. Deux (2)
  D. Chapelle (1) dominique.chapelle@inria.fr
• 关键词：Patient ; specific cardiac modeling – ; State and parameter estimation – ; Data assimilation – ; Filtering – ; Clinical data
• 刊名：Biomechanics and Modeling in Mechanobiology
• 出版年：2012
• 出版时间：May 2012
• 年：2012
• 卷：11
• 期：5
• 页码：609-630
• 全文大小：1.9 MB
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• 作者单位：1. INRIA, Rocquencourt, B.P. 105, 78153 Le Chesnay, France2. AP-HP H么pital Henri Mondor, Universit茅 Paris-Est Cr茅teil, Cr茅teil, France
• 刊物类别：Engineering
• 刊物主题：Theoretical and Applied Mechanics
  Biomedical Engineering
  Mechanics
  Biophysics and Biomedical Physics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1617-7940

文摘

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 heart model, 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 every measured 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.