Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics.
Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics.
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DOI：
10.1007/s10237-022-01571-8
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发表时间：
2022-06
影响因子：
3.5
通讯作者：
Gao, Hao
中科院分区：
文献类型：
--
作者：
Lazarus, Alan;Dalton, David;Husmeier, Dirk;Gao, Hao
关键词：
个性化的计算心脏模型被认为是现代心脏病学中一种独特而强大的工具，它将生理学、病理学和力学基本定律的知识整合在一个框架中。它们有可能改善心脏病患者的风险预测，并帮助开发新的治疗方法。然而，为了将这些模型用于临床决策支持，重要的是适当地量化模型参数扰动对感兴趣预测量的影响以及参数估计的不确定性，其中第一个任务本质上是先验的(意思是独立于任何特定的临床数据)，而第二个任务是后验的(即在获得特定的临床数据之后)。本研究解决了广泛使用的被动心肌本构定律(Holzapfel-Ogden模型)的这些挑战，使用全局灵敏度分析(SA)来解决第一个挑战，并使用逆不确定量化(I-UQ)来解决第二个挑战。利用计算效率高的高斯过程(GP)替代数值正向模拟器，对左心室(LV)模型的一系列不同输入参数进行SA。然后，SA的结果被用来通知所考虑的被动心肌的本构定律的低阶重新参数化。在观察到有噪声的实验数据的逆问题的背景下，这种参数化的质量然后通过I-UQ研究来量化，该研究同样利用GP代理模型。使用马尔科夫链蒙特卡罗以贝叶斯方式进行I-UQ，这允许对材料参数估计进行完全不确定量化。我们的研究揭示了SA和I-UQ之间的关系，阐明了参数敏感性和估计不确定性对外部因素的依赖，如LV腔内压力，并为心脏力学模型的建立提供了新的线索，尤其是Holzapfel-Ogden心肌模型。
Personalized computational cardiac models are considered to be a unique and powerful tool in modern cardiology, integrating the knowledge of physiology, pathology and fundamental laws of mechanics in one framework. They have the potential to improve risk prediction in cardiac patients and assist in the development of new treatments. However, in order to use these models for clinical decision support, it is important that both the impact of model parameter perturbations on the predicted quantities of interest as well as the uncertainty of parameter estimation are properly quantified, where the first task is a priori in nature (meaning independent of any specific clinical data), while the second task is carried out a posteriori (meaning after specific clinical data have been obtained). The present study addresses these challenges for a widely used constitutive law of passive myocardium (the Holzapfel-Ogden model), using global sensitivity analysis (SA) to address the first challenge, and inverse-uncertainty quantification (I-UQ) for the second challenge. The SA is carried out on a range of different input parameters to a left ventricle (LV) model, making use of computationally efficient Gaussian process (GP) surrogate models in place of the numerical forward simulator. The results of the SA are then used to inform a low-order reparametrization of the constitutive law for passive myocardium under consideration. The quality of this parameterization in the context of an inverse problem having observed noisy experimental data is then quantified with an I-UQ study, which again makes use of GP surrogate models. The I-UQ is carried out in a Bayesian manner using Markov Chain Monte Carlo, which allows for full uncertainty quantification of the material parameter estimates. Our study reveals insights into the relation between SA and I-UQ, elucidates the dependence of parameter sensitivity and estimation uncertainty on external factors, like LV cavity pressure, and sheds new light on cardio-mechanic model formulation, with particular focus on the Holzapfel-Ogden myocardial model.
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影响因子：
3.5
作者：
Asner, Liya;Hadjicharalambous, Myrianthi;Chabiniok, Radomir;Peresutti, Devis;Sammut, Eva;Wong, James;Carr-White, Gerald;Chowienczyk, Philip;Lee, Jack;King, Andrew;Smith, Nicolas;Razavi, Reza;Nordsletten, David
通讯作者：
Nordsletten, David
DOI：
10.1098/rsif.2017.0203
发表时间：
2017-07
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Gao H;Aderhold A;Mangion K;Luo X;Husmeier D;Berry C
通讯作者：
Berry C
影响因子：
9.7
作者：
Costabal, F. Sahli;Choy, J. S.;Kuhl, E.
通讯作者：
Kuhl, E.
影响因子：
4.1
作者：
Baillargeon, Brian;Rebelo, Nuno;Fox, David D.;Taylor, Robert L.;Kuhl, Ellen
通讯作者：
Kuhl, Ellen
影响因子：
10.9
作者：
Buoso, Stefano;Joyce, Thomas;Kozerke, Sebastian
通讯作者：
Kozerke, Sebastian