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

Research output: Contribution to journalConference paper

Original languageEnglish
Pages (from-to)357
Number of pages12
JournalLecture Notes in Computer Science
Volume10263
DOIs
StatePublished - 23 May 2017

King's Authors

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.

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