A POD-driven Machine Learning Algorithm to predict 3D Patient-Specific Aortic flows

Data-driven techniques are emerging as a credible alternative to traditional approaches like Computational Fluid Dynamics (CFD) to support personalised clinical decision-making processes to treat cardiovascular diseases. Here, we present a novel machine learning based approach to predict spatio-temporal 3D flow fields from clinical measurements such as 4DMR images in a patient-specific aorta.

The algorithm is driven by a Reduced Order Model (ROM) based on Proper Orthogonal Decomposition (POD). It consists of two neural networks, each trained separately. The first network is trained to predict POD coefficients directly from the flow rate at the aortic inlet, while the second network uses POD coefficient sequences from the past to predict future POD coefficients. Working synergistically, these networks can accurately predict POD coefficients throughout the entire cardiac cycle, even during diastole where traditional single network approaches fail to make reliable predictions because the blood flow rate is typically lower, providing less information. The predicted coefficients can then be used to compute velocity and pressure fields inside the 3D flow domain, allowing for more detailed medical analysis. This approach also has the potential for a wide range of applications beyond medical flow fields, extending to various domains of fluid dynamics.