Rapid prediction of prosthetic heart valve haemodynamic performance using physics-informed machine learning

使用基于物理的机器学习快速预测人工心脏瓣膜的血流动力学性能

• 批准号: 2633335
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2633335
• 关键词: Rapid; prediction; prosthetic; heart; valve

The project aims to create rapid and scalable deep learning-based simulation techniques for predicting the haemodynamic performance of transcatheter aortic valve (TAV) implants. TAVs are the de facto treatment for severe aortic valve stenosis in patients with med-/high-surgical risk. Yet, various clinical and technological challenges prevent uptake of the procedure in a wider spectrum of patients. Computational modelling tools can allow performance of TAVs in so-far untested scenarios (low-risk groups, bicuspid anatomy, etc.) to be assessed non-invasively and safely. Such capabilities underpin the emerging concept of in-silico trials (ISTs). Complex interactions of blood flow and valves necessitate use of fluid-structure interaction models for haemodynamic assessment. FSI models are expensive, and, like all computational techniques, can encounter convergence problems especially in complex scenarios. This is problematic for ISTs, where large simulation cohorts are required to run quickly and automatically. Recent research demonstrates the feasibility of accelerating computational multiphysics via learning-based approaches. So-called physics-informed neural networks (PINNs) are particularly attractive as they guarantee predictions conform to physical laws, rather than proceeding from data observations only. They may also alleviate training data requirements by imposing strong regularisation. The project will build on our existing efforts to develop distinct structural- and flow-based PINN models. The aim is to produce efficient and automated FSI simulation tools that will enable TAV haemodynamic performance to be predicted in simulation studies involving potentially thousands of virtual patients.

该项目旨在创建快速和可扩展的基于深度学习的模拟技术，用于预测经导管主动脉瓣（TAV）植入物的血流动力学性能。tav是医学/高手术风险患者严重主动脉瓣狭窄的实际治疗方法。然而，各种临床和技术挑战阻碍了更广泛的患者接受该手术。计算建模工具可以使tav在迄今尚未测试的场景（低风险人群、双尖齿解剖等）中的性能得到无创和安全的评估。这样的能力支撑了新兴的硅内试验（ist）概念。血流和瓣膜的复杂相互作用需要使用流体结构相互作用模型进行血流动力学评估。FSI模型是昂贵的，并且，像所有的计算技术一样，可能遇到收敛问题，特别是在复杂的场景中。这对于ist来说是个问题，因为它需要大量的模拟队列快速且自动地运行。最近的研究证明了通过基于学习的方法加速计算多物理场的可行性。所谓的物理信息神经网络（pinn）特别有吸引力，因为它们保证预测符合物理定律，而不是仅仅从数据观察出发。它们还可能通过实施强有力的规范化来减轻对训练数据的要求。该项目将以我们现有的努力为基础，开发独特的基于结构和流动的PINN模型。目的是生产高效和自动化的FSI模拟工具，使TAV血流动力学性能能够在涉及数千名虚拟患者的模拟研究中得到预测。