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.
@article{arxiv.2302.12835,
title = {Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI},
author = {Simone Saitta and Marcello Carioni and Subhadip Mukherjee and Carola-Bibiane Schönlieb and Alberto Redaelli},
journal= {arXiv preprint arXiv:2302.12835},
year = {2023}
}