Attention-guided super-resolution of 4D flow MRI in carotid arteries
Four-dimensional (4D) flow magnetic resonance imaging (MRI) is a powerful non-invasive technique for visualizing and quantifying complex blood flow patterns in vivo. Despite its clinical promise, broader adoption is limited by low spatial resolution and sensitivity to noise, which restrict accurate assessment of critical hemodynamic biomarkers such as wall shear stress, pressure gradients, and turbulent kinetic energy. To overcome these challenges, we propose a deep learning-based super-resolution framework that integrates multi-scale feature extraction and attention mechanisms to enhance the quality of 4D flow MRI data. The model was trained on a dataset of 120 patients with 240 stenosed...
| Authors | Ali Mokhtari, Dominik Obrist |
|---|---|
| Primary category | physics.med-ph |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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