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Abstract #1237

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

Tina Yao1, Endrit Pajaziti1, Michael Quail1, Jennifer Steeden1, and Vivek Muthurangu1
1Institute of Cardiovascular Science, University College London, London, United Kingdom

Synopsis

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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Keywords