Neural Network-based Classification of Aortic Stenosis Severity and Velocity Inlet Prediction from Cine 2D Flow MRI

Motivation: Imaging stenotic aortic valves using cine 2D and 4D Flow-MRI is compromised by flow-related image artefacts, making estimation of the effective orifice area challenging. Goal(s): To estimate aortic valve orifice area and inlet velocity profiles from 2D PC-MRI slices, acquired downstream of the aortic valve. Approach: Synthetic 2D PC-MRI slices were generated from personalized synthetic flow simulations of pulsatile flow in realistic stenosed aortae. Two U-Nets were trained to predict valvular orifice and inlet velocity profiles. Results: This work demonstrates that classification of aortic stenosis and prediction of peak systolic velocities from synthetic 2D PC-MRI slices acquired downstream of the valve is possible. Impact: Our work indicates that aortic valvular orifice area and inlet velocity profiles can indeed be predicted from a few cine 2D PC-MRI slices acquired downstream of the valve. The approach potentially enables time-efficient standard imaging using a few breathheld scans as available on all clinical MR systems.