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

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是中/高手术风险患者重度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。计算建模工具可以允许TAV在迄今为止未经测试的情况下（低风险组、双尖牙解剖结构等）的性能。进行非侵入性安全评估。这种能力支撑了计算机模拟试验（IST）的新兴概念。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液动力学评估。FSI模型是昂贵的，并且，像所有的计算技术，可以遇到收敛问题，特别是在复杂的情况下。这对于IST来说是个问题，因为需要快速自动地运行大型模拟队列。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，而不仅仅是从数据观察出发。它们还可以通过强正则化来减轻训练数据的要求。该项目将建立在我们现有的努力，以开发不同的结构和流动为基础的PINN模型。其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模拟研究中预测，涉及潜在的数千名虚拟患者。

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.