465-02-001 ISMRM Abstract

Uncertainty quantification and active learning for efficient super-resolution 4D Flow MRI

Primary: Analysis Methods - Image Enhancement

Secondary: Cardiovascular - Flow

465-02-001 · Novel Developments and Applications in Flow MRI · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row F

Keywords: 4D flow MRI Uncertainty quantification CONVOLUTIONAL NEURAL NETWORK Super Resolution Active learning

Accepted

Nathan Blanchard¹, David Marlevi ², Oliver Welin Odeback^1,2, Måns Magnusson³, Hannes Waldetoft³

¹Karolinska Institutet, Solna, Sweden

²Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden

³Uppsala University, Uppsala, Sweden

Presenting Author: David Marlevi

Synopsis

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References

1. Markl M, Frydrychowicz A, Kozerke S, et al. 4D flow MRI. J Magn Reson Imaging. 2012;36(5):1015-36.

2. Marlevi D, Ruijsink B, Balmus M, et al. Estimation of Cardiovascular Relative Pressure Using Virtual Work-Energy. Sci Rep. 2019;1375(9)

3. Szajer J, Ho-Shon K. A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations - A review. 2018;48:62-69

4. Ferdian E, Suinesiaputra A, Dubowitz DJ, et al. 4DFlowNet: Super-resolution 4D Flow MRI using deep learning and computational fluid dynamics. Frontiers in Physics. 2020:8:138

5. Callmer P, Bonini M, Ferdian E, Nordsleten D, Giese D, Young AA, Fyrdahl A, Marlevi D. Deep learning for temporal super-resolution 4D Flow MRI. arXiv. 2025

6. Callmer P, Bonini M, Ferdian E, Nordsleten D, Giese D, Young AA, Fyrdahl A, Marlevi D. Deep learning for temporal super-resolution 4D Flow MRI. arXiv. 2025

7. Cohn DA, Ghahramani Z, Jordan MI. Active learning with statistical models. J Artifical Intelligence Res. 1996(4)

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/465-02-001