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

Simone Saitta, Marcello Carioni, Subhadip Mukherjee, Carola-Bibiane Schönlieb, Alberto Redaelli
2/24/2023
eess.IVcs.CVcs.LG

No official code found. Best implementation: saitta-s/INRs-4DFlowMRI (5 stars, MIT, last commit 2023-04-30).

Abstract

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.

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Code Implementations(1)

saitta-s/INRs-4DFlowMRICommunity53%
MIT

Implict neural representations for 4D flow MRI denoising and super-resolution (preprint https://arxiv.org/pdf/2302.12835.pdf)

51PythonApr 30, 20233 years agoMIT

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Cite this paper

@article{saitta2023implicit,
  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},
  year   = {2023},
  eprint = {2302.12835},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.12835v1}
}

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