4D Flow MRI Velocity Enhancement and Anti-Aliasing Using Divergence Free Potential in Neural Networks

Motivation: 4D flow MRI suffers from different sources of noise and aliasing artifacts. However, the existing techniques for enhancing velocities in 4D flow MRI encounter reliability problems with varying flow patterns or acquisition parameters. Goal(s): Our goal was to develop a velocity enhancement an anti-aliasing technique for 4D Flow MRI that can be easily applied to diverse flow types. Approach: We incorporate a vector potential into a neural network to predict velocity fields that strictly adhere to the divergence-free condition. Results: Results from simulated 4D flow MRI images demonstrate significant noise reduction and aliasing correction. Impact: The proposed Physics-Informed Neural Network enables the recovery of noise-free and aliasing artifact-free velocity fields using divergence-free terms in the network without the need for tuning hyperparameters in the training function, enhancing the applicability of these networks to different datasets.