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4D flow MRI is a non-invasive imaging method that can measure blood flowvelocities over time. However, the velocity fields detected by this techniquehave limitations due to low resolution and measurement noise. Coordinate-basedneural networks have been researched to improve accuracy, with SIRENs beingsuitable for super-resolution tasks. Our study investigates SIRENs fortime-varying 3-directional velocity fields measured in the aorta by 4D flowMRI, achieving denoising and super-resolution. We trained our method on voxelcoordinates and benchmarked our approach using synthetic measurements and areal 4D flow MRI scan. Our optimized SIREN architecture outperformedstate-of-the-art techniques, producing denoised and super-resolved velocityfields from clinical data. Our approach is quick to execute and straightforwardto implement for novel cases, achieving 4D super-resolution.

Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI