On modeling the in vivo ventricular passive mechanical behavior from in vitro experimental properties in porcine hearts

COMPUTERS & STRUCTURES(2024)

引用 0|浏览5
暂无评分
摘要
Myocardium passive mechanical response has been a major topic of study for decades due to its major impact on cardiac physiology. Here, we propose a novel modeling methodology that integrates both in vivo and in vitro data to estimate the tissue mechanical parameters for a particular orthotropic hyperelastic model as those proposed by Costa and by Holzapfel & Ogden, although it can be easily extended to any other. In vitro biaxial and triaxial shear extension tests were conducted in biopsied samples and in vivo pressure-volume recordings were obtained. Left ventricle (LV) geometry was reconstructed using magnetic resonance imaging (MRI) and pressure gradients during ventricular inflation were recorded with the Catheter Conductance Method (CCM). Finally, a Finite Element (FE) in vivo LV model was implemented to get the material model parameters using an inverse approach that uses a minimization process combining both the in vivo and in vitro available data. Our results demonstrate that the parameters obtained solely from in vitro testing (IVT), or from in vivo passive inflation (IVV) do not provide satisfactory fits for both responses simultaneously (R-IVT(2tests) = 0.977, R2(IVT)(PV) = 0.697 and R-IVV(2tests) = 0.687,R-IVV(2PV) = 0.995). On the contrary, the proposed combined in vitro & in vivo optimization process (MIN) converges to a solution that effectively captures both the in vivo and in vitro behaviors (R-MIN(2tests) = 0.815, R-MIN(2PV) = 0.992). Thus, this novel combined approach offers a comprehensive framework for accurately characterizing myocardial mechanical behavior. The obtained parameters can serve as a basis for further cardiac simulations and contribute to a better understanding of cardiac mechanics and function.
更多
查看译文
关键词
Cardiac tissue,Myocardial mechanical behavior,Patient specific,Porcine heart,In vivo imaging,In vitro tests,Inverse approach
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要