Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI. (arXiv:2302.12835v1 [eess.IV])

4D flow MRI is a non-invasive imaging method that can measure blood flow
velocities over time. However, the velocity fields detected by this technique
have limitations due to low resolution and measurement noise. Coordinate-based
neural networks have been researched to improve accuracy, with SIRENs being
suitable for super-resolution tasks. Our study investigates SIRENs for
time-varying 3-directional velocity fields measured in the aorta by 4D flow
MRI, achieving denoising and super-resolution. We trained our method on voxel
coordinates and benchmarked our approach using synthetic measurements and a
real 4D flow MRI scan. Our optimized SIREN architecture outperformed
state-of-the-art techniques, producing denoised and super-resolved velocity
fields from clinical data. Our approach is quick to execute and straightforward
to implement for novel cases, achieving 4D super-resolution.