Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
607-01-002 ISMRM Abstract

Deep-Learning Based Highly Sub-Sampled 2-Point Velocity Encoding 4D flow MRI

Accepted
Haben Berhane 1,2, Ethan Johnson3, Sebastian Cohn1,4, Bradley D Allen4, Michael Markl1,2,3,5
1Biomedical Engineering, Northwestern University, Chicago, United States of America
2Radiology, Feinberg School of Medicine, Northwestern Medicine, Chicago, United States of America
3Northwestern University, Chicago, United States of America
4Northwestern University Feinberg School of Medicine, Chicago, United States of America
5Radiology, Northwestern University, Chicago, United States of America
Presenting Author: Haben Berhane

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Ma, L.E., et al., Aortic 4D flow MRI in 2 minutes using compressed sensing, respiratory controlled adaptive k-space reordering, and inline reconstruction. Magn Reson Med, 2019. 81(6): p. 3675-3690. DOI: 10.1002/mrm.27684. [doi]
2. Pathrose, A., et al., Highly accelerated aortic 4D flow MRI using compressed sensing: Performance at different acceleration factors in patients with aortic disease. Magn Reson Med, 2021. 85(4): p. 2174-2187. DOI: 10.1002/mrm.28561. [doi]
3. Berhane, H., et al., Deep-Learning based Super-resolution 4D Flow MRI, in SMRA 2023. 2023.
4. Berhane, H., et al., Fluid-Physics Informed Deep-Learning Enabled 2-point Velocity Encoded 4D flow MRI, in ISMRM 2024. 2024.
5. Kim, D., et al., Accelerated 4D-flow MRI with 3-point encoding enabled by machine learning. Magn Reson Med, 2023. 89(2): p. 800-811. DOI: 10.1002/mrm.29469. [doi]
6. Berhane, H., et al., Anatomy-derived 3D Aortic Hemodynamics Using Fluid Physics-informed Deep Learning. Radiology, 2025. 315(2): p. e240714. DOI: 10.1148/radiol.240714. [doi]
7. Johnson, E.M.I., et al., A Fully Automated Analysis Pipeline for 4D Flow MRI in the Aorta. Bioengineering (Basel), 2025. 12(8). DOI: 10.3390/bioengineering12080807. [doi]

Cite this abstract