Feasibility of the Estimation of Myocardial Stiffness with Reduced 2D Deformation Data

  • Anastasia Nasopoulou
  • David A. Nordsletten
  • Steven A. Niederer
  • Pablo Lamata
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10263)

Abstract

Myocardial stiffness is a useful diagnostic and prognostic biomarker, but only accessible through indirect surrogates. Computational 3D cardiac models, through the process of personalization, can estimate the material parameters of the ventricles, allowing the estimation of stiffness and potentially improving clinical decisions. The availability of detailed 3D cardiac imaging data, which are not routinely available for the conventional cardiologist, is nevertheless required to constrain these models and extract a unique set of parameters. In this work we propose a strategy to provide the same ability to identify the material parameters, but from 2D observations that are obtainable in the clinic (echocardiography). The solution combines the adaptation of an energy-based cost function, and the estimation of the out of plane deformation based on an incompressibility assumption. In-silico results, with an analysis of the sensitivity to errors in the deformation, fibre orientation, and pressure data, demonstrate the feasibility of the approach.

Keywords

Cardiac mechanics Myocardial stiffness Energy-based cost function Parameter estimation 2D images 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anastasia Nasopoulou
    • 1
  • David A. Nordsletten
    • 1
  • Steven A. Niederer
    • 1
  • Pablo Lamata
    • 1
  1. 1.Division of Imaging Sciences and Biomedical Engineering, Department of Biomedical EngineeringKing’s College LondonLondonUK

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