Image2Flow: Fast Calculation of Pulmonary Artery Flow Fields from 3D Cardiac MRI Using Graph Convolutional Neural Networks

Motivation: Computational fluid dynamics (CFD) is used for non-invasive cardiovascular hemodynamic assessment, but it is limited by time-consuming manual segmentation and expertise needed for simulation. Goal(s): Improve the speed and simplify volume mesh generation and CFD flow field calculation. Approach: Develop a single deep-learning model capable of reconstructing the pulmonary artery from a 3D cardiac MRI as a volume mesh and predicting CFD-like pressure and flow. Results: Our model achieves accurate pulmonary artery reconstruction with a median Dice score of 0.9. It computes CFD-like pressure and flow with median errors of 14.9% and 9.0%, respectively. Our model is ~10,000 times faster than manual calculation. Impact: Image2Flow is a single-pass deep-learning model that rapidly and accurately reconstructs pulmonary artery volume meshes from 3D cardiac MR and predicts CFD-like flow fields. Our model can potentially streamline and expedite cardiovascular haemodynamic assessment and facilitate more efficient treatment planning.