Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
465-02-012 ISMRM Abstract

Deep learning based gold-standard background offset correction of cardiovascular 4D flow MRI: Evaluation on a clinical cohort

Primary: Cardiovascular - Flow
Secondary: Analysis Methods - Data Processing
465-02-012 · Novel Developments and Applications in Flow MRI · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row F
Keywords: 4D flow MRI Deep learning Cardiovascular magnetic resonance Phase correction Background phase offset
Accepted
Federica Viola 1,2, Chiara Trenti1,2, Mattias Ekstedt1,2, Farkas Vanky1,3, Peter Lundberg1,2,4,5, Nils Dahlström1,2,5, Patrik Nasr1,2,6, Carl-Johan Carlhäll1,2,7, Petter Dyverfeldt1,2,8, Tino Ebbers1,2,8
1Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
2Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
3Clinical Department of Cardiothoracic and Vascular Surgery, Region Östergötland, Linköping, Sweden
4Clinical Department of Medical Radiation Physics in Linköping, Region Östergötland, Linköping, Sweden
5Clinical Department of Radiology in Linköping, Region Östergötland, Linköping, Sweden
6Wallenberg Center for Molecular Medicine, Linköping University, Linköping, Sweden
7Clinical Department of Clinical Physiology in Linköping, Region Östergötland, Linköping, Sweden
8Science for Life Laboratory, Linköping University, Linköping, Sweden
Presenting Author: Federica Viola

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. Rolf MP, Hofman MB, Gatehouse PD, et al. Sequence optimization to reduce velocity offsets in cardiovascular magnetic resonance volume flow quantification - A multi-vendor study. Journal of Cardiovascular Magnetic Resonance 2011;13(1):18. https://doi.org/10.1186/1532-429X-13-18. [doi]
2. Giese D, Haeberlin M, Barmet C, Pruessmann KP, Schaeffter T, Kozerke S. Analysis and correction of background velocity offsets in phase-contrast flow measurements using magnetic field monitoring. Magnetic Resonance in Medicine 2012;67(5):1294–302. https://doi.org/10.1002/mrm.23111. [doi]
3. Loecher M, Schrauben E, Johnson KM, Wieben O. Phase unwrapping in 4D MR flow with a 4D single-step laplacian algorithm. Journal of Magnetic Resonance Imaging 2016;43(4):833–42. https://doi.org/10.1002/jmri.25045. [doi]
4. Lankhaar J-W, Hofman MBM, Marcus JT, Zwanenburg JJM, Faes TJC, Vonk-Noordegraaf A. Correction of phase offset errors in main pulmonary artery flow quantification. Journal of Magnetic Resonance Imaging 2005;22(1):73–9. https://doi.org/10.1002/jmri.20361. [doi]
5. Walker PG, Cranney GB, Scheidegger MB, Waseleski G, Pohost GM, Yoganathan AP. Semiautomated method for noise reduction and background phase error correction in MR phase velocity data. Journal of Magnetic Resonance Imaging : JMRI 1993;3(3):521–30.
6. Ebbers T, Haraldsson H, Dyverfeldt P. Higher order weighted least-squares phase offset correction for improved accuracy in phase-contrast MRI. Proceedings of the International Society of Magnetic Resonance in Medicine 16 2008;39:1367.
7. Bissell MM, Raimondi F, Ait Ali L, et al. 4D Flow cardiovascular magnetic resonance consensus statement: 2023 update. Journal of Cardiovascular Magnetic Resonance 2023;25(1):40. https://doi.org/10.1186/s12968-023-00942-z. [doi]
8. Viola F, Trenti C, Ekstedt M, et al. Automatic background offset correction of cardiovascular 4D flow MRI data using Deep Learning. Proceedings of the 34th Annual Meeting of ISMRM 2025.
9. Tampu IE, Eklund A, Haj-Hosseini N. Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images. Sci Data 2022;9(1):580. https://doi.org/10.1038/s41597-022-01618-6. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/465-02-012