Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI

• URL: http://arxiv.org/abs/2508.14950v1
• Date: Wed, 20 Aug 2025 12:07:23 GMT
• Title: Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
• Authors: Oliver Welin Odeback, Arivazhagan Geetha Balasubramanian, Jonas Schollenberger, Edward Ferdiand, Alistair A. Young, C. Alberto Figueroa, Susanne Schnell, Outi Tammisola, Ricardo Vinuesa, Tobias Granberg, Alexander Fyrdahl, David Marlevi,
• Abstract summary: Generative adversarial networks (GANs) offer strong capabilities in restoring sharp boundaries in non-medical super-resolution tasks.<n>Generative adversarial networks (GANs) offer a compelling solution, having demonstrated strong capabilities in restoring sharp boundaries in non-medical super-resolution tasks.<n>Our results demonstrate that the proposed GAN improved near-wall velocity recovery compared to a non-adversarial reference.
• Score: 31.73599420656492
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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. Generative adversarial networks (GANs) offer a compelling solution, having demonstrated strong capabilities in restoring sharp boundaries in non-medical super-resolution tasks. Yet, their application in 4D Flow MRI remains unexplored, with implementation challenged by known issues such as training instability and non-convergence. In this study, we investigate GAN-based super-resolution in 4D Flow MRI. Training and validation were conducted using patient-specific cerebrovascular in-silico models, converted into synthetic images via an MR-true reconstruction pipeline. A dedicated GAN architecture was implemented and evaluated across three adversarial loss functions: Vanilla, Relativistic, and Wasserstein. Our results demonstrate that the proposed GAN improved near-wall velocity recovery compared to a non-adversarial reference (vNRMSE: 6.9% vs. 9.6%); however, that implementation specifics are critical for stable network training. While Vanilla and Relativistic GANs proved unstable compared to generator-only training (vNRMSE: 8.1% and 7.8% vs. 7.2%), a Wasserstein GAN demonstrated optimal stability and incremental improvement (vNRMSE: 6.9% vs. 7.2%). The Wasserstein GAN further outperformed the generator-only baseline at low SNR (vNRMSE: 8.7% vs. 10.7%). These findings highlight the potential of GAN-based super-resolution in enhancing 4D Flow MRI, particularly in challenging cerebrovascular regions, while emphasizing the need for careful selection of adversarial strategies.

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