Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI
This work addresses noise and resolution issues in 4D flow MRI for medical imaging applications, representing an incremental improvement using existing methods on new data.
The study tackled the problem of low resolution and noise in 4D flow MRI velocity fields by applying SIREN-based implicit neural representations, achieving denoising and super-resolution that outperformed state-of-the-art techniques on clinical data.
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.