Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
607-01-009 ISMRM Abstract

PUDIP-Flow: An Unsupervised and Segmentation-Free Phase Unwrapping Method for Aortic and Cerebrovascular 4D Flow MRI

Primary: Cardiovascular - Flow
Secondary: Analysis Methods - Data Processing
607-01-009 · Advanced Cardiac Quantification Methods · Thursday, 14 May, 8:30 AM–10:20 AM · Meeting Room 1.40
Keywords: 4D flow MRI Deep Image Prior Unsupervised learning Phase unwrapping
Accepted
Yuyang Ren 1, Zijian Zhou1, Yuan Feng2,3,4, Yining Wang5, Peng Hu1,6,7
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
2Institute of Medical Robotics, Shanghai JiaoTong University, Shanghai, China
3National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai JiaoTong University, Shanghai, China
4School of Biomedical Engineering, Shanghai JiaoTong University, Shanghai, China
5Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
6Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
7State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
Presenting Author: Yuyang Ren

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References

1. Markl, M., et al., 4D flow MRI. 2012. 36(5): p. 1015-1036. https://doi.org/10.1002/jmri.23632 [doi]
2. Nett, E.J., et al., Four‐dimensional phase contrast MRI with accelerated dual velocity encoding. Journal of Magnetic Resonance Imaging, 2012. 35(6): p. 1462-1471. https://doi.org/10.1002/jmri.23588 [doi]
3. Loecher, M., et al., Phase unwrapping in 4D MR flow with a 4D single‐step laplacian algorithm. Journal of Magnetic Resonance Imaging, 2016. 43(4): p. 833-842. https://doi.org/10.1002/jmri.25045. [doi]
4. Abdul-Rahman, H., et al. Fast three-dimensional phase-unwrapping algorithm based on sorting by reliability following a non-continuous path. in Optical measurement systems for industrial inspection IV. 2005. SPIE. https://doi.org/10.1364/AO.46.006623 [doi]
5. Bioucas-Dias, J.M. and G. Valadao, Phase unwrapping via graph cuts. IEEE Transactions on Image processing, 2007. 16(3): p. 698-709. https://doi.org/10.1109/TIP.2006.888351 [doi]
6. Yang, F., et al., Robust phase unwrapping via deep image prior for quantitative phase imaging. IEEE Transactions on Image Processing, 2021. 30: p. 7025-7037. https://doi.org/10.1109/TIP.2021.3099956 [doi]
7. Ronneberger, O., P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. 2015. Springer. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
8. Vishnevskiy, V., J. Walheim, and S.J.N.M.I. Kozerke, Deep variational network for rapid 4D flow MRI reconstruction. 2020. 2(4): p. 228-235. https://doi.org/10.1038/s42256-020-0165-6 [doi]
9. Dirix, P., L. Jacobs, and S. Kozerke, 4Dflow-unwrap: A collection of python-based phase unwrapping algorithms. Proceedings of 24th Annual Meeting ISMRM, 2024.

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http://echo.ismrm.org/p/ISMRM2026/607-01-009