{"ID":2880244,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14950","arxiv_id":"2508.14950","title":"Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI","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.","short_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 promi...","url_abs":"https://arxiv.org/abs/2508.14950","url_pdf":"https://arxiv.org/pdf/2508.14950v2","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\"]","published":"2025-08-20T12:07:23Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}