AI RESEARCH
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
arXiv CS.LG
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ArXi:2508.14950v2 Announce Type: replace-cross 4D Flow Magnetic Resonance Imaging (4D Flow MRI) enables non-invasive quantification of blood flow and hemodynamic parameters. However, its clinical application is limited by low spatial resolution and noise, particularly affecting near-wall velocity measurements. Machine learning-based super-resolution has shown promise in addressing these limitations, but challenges remain, not least in recovering near-wall velocities.