Myocardium Biomechanical Modelling and Myocardial Contraction Force Reconstruction
Myocardium Biomechanical Modelling and Myocardial Contraction Force Reconstruction
批准号:
RGPIN-2014-06050
负责人:
Samani, Abbas
金额:
$2.4万
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31
中文摘要
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英文摘要
Computational models of the myocardium (heart muscle), in particular the left ventricle (LV), are effective tools that can be used to study its mechanics and to gain insight into its physiology. Unlike many other tissues in the body, the beating heart is both active and passive such that the active component generates coordinated muscle contraction resulting from the heart’s electrophysiology while its passive component responds to external forces and its own generated contraction. Combined with the tissue complex intrinsic properties, the complexity of the active component and its coupling with the heart’s electrophysiological activity has made developing reliable models very challenging. Over the last many decades, researchers have developed computational cardiac models with various levels of sophistication ranging from simple passive linear elastic isotropic models to highly complex passive/active hyperelastic anisotropic models. While the latter models are promising as they agree reasonably well with experimental data they are often based on very complex algorithms, rendering its development a daunting task that require demanding time and resources. Furthermore, no commercial software is available that is capable of simulating the active/passive components of the beating heart mechanics. The very limited availability of such computational tools has prevented accelerated progress in studying cardiac mechanics and ultimately impacting the development of much needed effective tools to understand the myocardium mechanics and its physiology. One potential solution to address this issue from an engineering perspective is to develop a novel paradigm which enables developing computational cardiac mechanics models using traditional Finite Elements (FE) formulation such that commercial FE software engine and modules are integrated into a software package to be used for studying cardiac mechanics. We propose to address the limitations of current cardiac mechanics models by developing a new FE formulation based on a novel paradigm. This paradigm makes possible the utility of commercial FE software engine and modules for cardiac mechanics model development to develop highly accurate software tool for cardiac mechanics simulation. This model idealizes the myocardium as a composite material with myofibers surrounded by a complex background that mimics the tissue extracellular matrix. The myofibers of the beating heart will be modeled as hyperelastic prestressed rods with known time varying prestress. The extracellular matrix will be idealized as a hyperelastic material consistent with known mechanical properties of its multi constituents. The developed model will be tested using experimentally-derived measurements. It will be used as forward model in an inverse problem framework for contraction force reconstruction. These forces will be reconstructed using contraction displacement data derived from imaging. These forces can be used to further understand various pathologies (e.g. pathologies associated with arrhythmia and myocardial infarction). Finally, electromechanical coupling model will be developed where the developed FE model of the cardiac mechanics will be incorporated. The developed forward and inverse models can be used for furthering our understanding of the beating heart mechanics. They can play an important role in addressing a wide range of fundamental scientific questions regarding the heart physiology and gaining insight into pathways of pathological conditions. For example, they can be applied in computer simulation of cardiac resynchronization therapy used to treat patients with congestive heart failure. This simulation enables testing various therapy scenarios, paving the way for achieving optimal outcome.
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