Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
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.
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