Adaptive, Multi-scale, Data-Infused Biomechanical Models for Cardiac Diagnostic and Prognostic Assessment

用于心脏诊断和预后评估的自适应、多尺度、数据注入生物力学模型

• 批准号: EP/R003866/1
• 负责人: David Nordsletten
• 金额: $ 143.16万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR003866%2F1
• 关键词: Adaptive; Multi; scale; Data; Infused

Driven by structural and functional abnormalities in the muscle of the heart, Heart Failure (HF) is a complex syndrome affecting around 900,000 people in the UK. HF results in a fundamental reduction in the ability of heart muscle to effectively pump and deliver blood to the body. While this deficiency in pump function is easily observed using medical imaging, dissecting the underlying cause of this reduced performance in terms of its implications for muscle structure and function remain open challenges. Further, predicting how disease will progress or respond to therapy remains an unmet need that would substantially improve patient care.Using state-of-the art biomechanical modelling, the "Adaptive, Multi-scale, Data-Infused Biomechanical Models for Cardiac Diagnostic and Prognostic Assessment" project will address these challenges by providing a modelling framework for assessment of the heart. Uniting measurements from microscopy, rheology, and medical imaging, this project aims to create biomechanical models that provide detailed information on the structure and function of the heart aiding diagnosis. Further, the platform will provide infrastructure for predictive modelling, simulating the response and adaptation of the heart over time. Biomechanical models will be systematically validated using animal heart models, providing rich data for understanding the biomechanics in vivo and ex vivo. The framework will, further, be directly translated through a novel study in patients with hypertrophic cardiomyopathy, providing a test bed for validation of predictive models of disease progression and response to therapy through virtual surgery.

在心脏肌肉结构和功能异常的推动下，心力衰竭(HF)是一种复杂的综合征，在英国约有90万人受到影响。心衰导致心肌有效泵入和输送血液到体内的能力从根本上降低。虽然泵功能的这种缺陷很容易通过医学成像被观察到，但从其对肌肉结构和功能的影响来剖析这种性能下降的根本原因仍然是一个开放的挑战。此外，预测疾病的进展或对治疗的反应仍然是一个未得到满足的需求，将极大地改善患者的护理。使用最先进的生物力学建模，“用于心脏诊断和预后评估的自适应、多尺度、注入数据的生物力学模型”项目将通过为心脏评估提供建模框架来解决这些挑战。结合显微镜、流变学和医学成像的测量，该项目旨在创建生物力学模型，提供有关心脏结构和功能的详细信息，以帮助诊断。此外，该平台将为预测性建模提供基础设施，模拟心脏随时间的反应和适应。生物力学模型将使用动物心脏模型进行系统验证，为理解体内和体外生物力学提供丰富的数据。此外，该框架还将通过一项针对肥厚型心肌病患者的新研究直接翻译，为通过虚拟手术验证疾病进展和治疗反应的预测模型提供一个试验台。

项目成果

Magnetic resonance elastography in nonlinear viscoelastic materials under load.

• DOI: 10.1007/s10237-018-1072-1
• 发表时间: 2019-03
• 期刊: Biomechanics and modeling in mechanobiology
• 影响因子: 3.5
• 作者: Capilnasiu A;Hadjicharalambous M;Fovargue D;Patel D;Holub O;Bilston L;Screen H;Sinkus R;Nordsletten D
• 通讯作者: Nordsletten D

Nonlinear viscoelastic constitutive model for bovine liver tissue.

• DOI: 10.1007/s10237-020-01297-5
• 发表时间: 2020-10
• 期刊: Biomechanics and modeling in mechanobiology
• 影响因子: 3.5
• 作者: Capilnasiu A;Bilston L;Sinkus R;Nordsletten D
• 通讯作者: Nordsletten D

A stabilized multidomain partition of unity approach to solving incompressible viscous flow

求解不可压缩粘性流的稳定多域统一方法划分

• DOI: 10.1016/j.cma.2022.114656
• 发表时间: 2022
• 期刊: Computer Methods in Applied Mechanics and Engineering
• 影响因子: 7.2
• 作者: Balmus M

Hemodynamic Modeling for Mitral Regurgitation

二尖瓣反流的血流动力学建模

• DOI: 10.1016/j.healun.2022.01.1685
• 发表时间: 2022
• 期刊: The Journal of Heart and Lung Transplantation
• 作者: Bonini M

CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation.

深红色：用于心血管集成建模和仿真的开源软件框架。

• DOI: 10.1371/journal.pcbi.1008881
• 发表时间: 2021-05
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Arthurs CJ;Khlebnikov R;Melville A;Marčan M;Gomez A;Dillon-Murphy D;Cuomo F;Silva Vieira M;Schollenberger J;Lynch SR;Tossas-Betancourt C;Iyer K;Hopper S;Livingston E;Youssefi P;Noorani A;Ben Ahmed S;Nauta FJH;van Bakel TMJ;Ahmed Y;van Bakel PAJ;Mynard J;Di Achille P;Gharahi H;Lau KD;Filonova V;Aguirre M;Nama N;Xiao N;Baek S;Garikipati K;Sahni O;Nordsletten D;Figueroa CA
• 通讯作者: Figueroa CA