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A whole-heart model of multiscale soft tissue mechanics and fluid structure interaction for clinical applications (Whole-Heart-FSI)

用于临床应用的多尺度软组织力学和流体结构相互作用的全心脏模型（Whole-Heart-FSI）

基本信息

批准号：
EP/S020950/1
负责人：
XiaoYu Luo
金额：
$ 166.25万
依托单位：
University of Glasgow
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FS020950%2F1
关键词：
whole heart model multiscale soft

项目摘要

Heart disease is the leading cause of disability and death in the UK and worldwide, resulting in enormous health care costs. Risk prediction on an individual patient basis is imperfect. Advanced medical development has already saved many lives, particularly in systolic heart failure. However, there is currently no treatment option for diastolic heart failure (with preserved ejection fraction) due to its complexity of multiple mechanisms and co-modality. Structural heart diseases, such as myocardial infarction (MI- commonly known as heart attack) and mitral regurgitation (MR, a leakage of blood through the mitral valve to left atrium in systole), where biomechanical factors are crucial, are often precursors to heart failure. MI can eventually lead to dilated heart failure despite immediate treatments post-MI. MR can induce pulmonary hypertension and oedema and subsequently, right heart overload and heart failure. The grand challenge is for these situations the heart simply cannot be modelled as an isolated left ventricle (as in most of the current studies); flow-structure interaction (FSI), heart-valve interaction, multiscale soft tissue mechanics, and tissue growth and remodelling (G&R) all play important roles in the progression of the structural diseases. This project is set up to meet this challenge by delivering a multiscale computational framework to include Whole-Heart FSI with G&R. Making use of the novel mathematical tools (constitutive laws, G&R, upscaling and statistical inference) developed by SofTMech, I will build a realistic four-chamber heart model that include heart-valve, chamber-chamber, heart-blood, and heart-circulation interactions, which will be powerful enough to model MI, MR and their pathological consequences. This work will be in close collaboration with my clinical, industrial and academic collaborators. The model will quantify which factors lead to adverse G&R and what variations are to be expected as the disease progresses. We will also identify significant biomechanical markers (e.g. constitutive parameters, energy indices, stress/strain evolution). The predictive values of these biomechanical parameters will be assessed against other established predictors of adverse remodellings, such as duration of ischaemia, final coronary flow grade after a primary percutaneous coronary intervention, and microvascular obstruction revealed by MRI. Thus, this project will generate new testable hypotheses and will be a significant step up towards more consistent decision-support for clinicians, since increasingly the pace and complexity of medical advances outstrip the ability of individual clinicians to cope with. Due to the statistical emulation and uncertainty quantification built into the project, the model predictions will be fast and quantified with error bounds on the outcome of alternative treatments. Consequently, we will also address the critical aspect of convincing clinicians that information obtained from simulations will be correct and relevant to their daily practice. The proposed research is right within the Healthcare Technologies "Optimising Treatment" and "Developing Future Therapies" priority areas, as well as targeting "New Connections from Mathematical Sciences", and "Statistics and Applied Probability" of Mathematical Sciences.

心脏病是英国和世界范围内残疾和死亡的主要原因，导致巨大的医疗保健费用。基于个体患者的风险预测是不完善的。先进的医学发展已经挽救了许多生命，特别是在收缩性心力衰竭方面。然而，由于舒张性心力衰竭（射血分数保留）的多种机制和共同模态的复杂性，目前没有治疗选择。结构性心脏病，如心肌梗死（MI-通常称为心脏病发作）和二尖瓣返流（MR，收缩期血液通过二尖瓣渗漏到左心房），其中生物力学因素至关重要，通常是心力衰竭的前兆。尽管MI后立即治疗，但MI最终可导致扩张性心力衰竭。二尖瓣返流可引起肺动脉高压和水肿，继而导致右心负荷过重和心力衰竭。最大的挑战是，对于这些情况，心脏根本不能被建模为一个孤立的左心室（如在大多数目前的研究）;流动-结构相互作用（FSI），心脏瓣膜相互作用，多尺度软组织力学，组织生长和重塑（G&R）都在结构性疾病的进展中发挥重要作用。该项目旨在通过提供一个多尺度计算框架来应对这一挑战，以包括具有G&R的全心脏FSI。利用SofTMech开发的新颖的数学工具（本构律，G&R，放大和统计推断），我将建立一个逼真的四腔心脏模型，包括心脏瓣膜，腔室，心脏血液和心脏循环的相互作用，这将是强大的，足以模拟MI，MR及其病理后果。这项工作将与我的临床，工业和学术合作者密切合作。该模型将量化哪些因素导致不良G&R以及随着疾病进展预期会发生哪些变化。我们还将确定重要的生物力学标记（例如，本构参数，能量指数，应力/应变演变）。这些生物力学参数的预测值将根据其他已确定的不良重塑预测因子进行评估，例如缺血持续时间、初次经皮冠状动脉介入治疗后的最终冠状动脉血流分级和MRI显示的微血管阻塞。因此，该项目将产生新的可测试的假设，并将是朝着更一致的决策支持临床医生迈出的重要一步，因为越来越多的速度和复杂性的医学进步超出了个人临床医生的能力，以科普。由于项目中内置的统计仿真和不确定性量化，模型预测将是快速的，并对替代治疗的结果进行量化。因此，我们还将解决说服临床医生从模拟中获得的信息将是正确的，并与他们的日常实践相关的关键方面。拟议的研究是正确的医疗保健技术“优化治疗”和“开发未来疗法”的优先领域，以及针对“数学科学的新联系”和“统计和应用概率”的数学科学。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Fluid-structure interaction in a fully coupled three-dimensional mitral-atrium-pulmonary model.

DOI：
10.1007/s10237-021-01444-6
发表时间：
2021-08
期刊：
Biomechanics and modeling in mechanobiology
影响因子：
3.5
作者：
Feng L;Gao H;Qi N;Danton M;Hill NA;Luo X
通讯作者：
Luo X

Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium.

基于机器学习方法的左心室心肌参数估计替代模型

DOI：
10.1098/rsos.201121
发表时间：
2021-01
期刊：
Royal Society open science
影响因子：
3.5
作者：
Cai L;Ren L;Wang Y;Xie W;Zhu G;Gao H
通讯作者：
Gao H

Nonlinear indentation of second-order hyperelastic materials

DOI：
10.1016/j.jmps.2022.105139
发表时间：
2022-09
期刊：
Journal of the Mechanics and Physics of Solids
影响因子：
5.3
作者：
Yangkun Du;P. Stewart;N. Hill;Huabing Yin;R. Penta;J. Köry;Xiaoyu Luo;R. Ogden
通讯作者：
Yangkun Du;P. Stewart;N. Hill;Huabing Yin;R. Penta;J. Köry;Xiaoyu Luo;R. Ogden

Fluid-structure interaction simulation of pathological mitral valve dynamics in a coupled mitral valve-left ventricle model

MV-LV 耦合模型中病态 MV 动力学的流固耦合模拟