| Autor: |
Suprosanna Shit, Judith Zimmermann, Ivan Ezhov, Johannes C. Paetzold, Augusto F. Sanches, Carolin Pirkl, Bjoern H. Menze |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Frontiers in Artificial Intelligence, Vol 5 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2624-8212 |
| DOI: |
10.3389/frai.2022.928181 |
| Popis: |
Exploiting 4D-flow magnetic resonance imaging (MRI) data to quantify hemodynamics requires an adequate spatio-temporal vector field resolution at a low noise level. To address this challenge, we provide a learned solution to super-resolve in vivo 4D-flow MRI data at a post-processing level. We propose a deep convolutional neural network (CNN) that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible. A novel, direction-sensitive, and robust loss function is crucial to learning vector-field data. We present a detailed comparative study between the proposed super-resolution and the conventional cubic B-spline based vector-field super-resolution. Our method improves the peak-velocity to noise ratio of the flow field by 10 and 30% for in vivo cardiovascular and cerebrovascular data, respectively, for 4 × super-resolution over the state-of-the-art cubic B-spline. Significantly, our method offers 10x faster inference over the cubic B-spline. The proposed approach for super-resolution of 4D-flow data would potentially improve the subsequent calculation of hemodynamic quantities. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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