465-02-004 ISMRM Abstract

Accurate Quantification of TKE using Deep Learning-Reconstructed Highly Undersampled 4D Flow MRI using FlowVN

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Cardiovascular - Flow

465-02-004 · Novel Developments and Applications in Flow MRI · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row F

Keywords: Image Reconstruction 4D flow MRI Cardiac MRI Deep learning reconstruction Turbulent kinetic energy

Accepted

Sohaib A Qazi ^1,2, Tamara Bianchessi^1,2, Federica Viola^1,2, Chiara Trenti^1,2, Erik Ylipää², Tino Ebbers^1,2,3, Petter Dyverfeldt^1,2,3

¹Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden

²Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden

³Science for Life Laboratory, Linköping University, Linköping, Sweden

Presenting Author: Sohaib A Qazi

Synopsis

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References

1. Bianchessi, T., Trenti, C., Carlhäll, C.J. et al. 4D Flow MRI Velocity and Turbulence Mapping in Mild Valvular Heart Disease. Journal of Magnetic Resonance Imaging. 2025. https://doi.org/10.1002/jmri.70087 [doi]

2. Binter, C., Gotschy, A., Sündermann, S.H., et al., Turbulent kinetic energy assessed by multipoint 4-dimensional flow magnetic resonance imaging provides additional information relative to echocardiography for the determination of aortic stenosis severity. Circulation: Cardiovascular Imaging, 2017; 10(6), p.e005486. https://doi.org/10.1161/circimaging.116.005486 [doi]

3. Dyverfeldt P, Kvitting JP, Sigfridsson A, et al. Assessment of fluctuating velocities in disturbed cardiovascular blood flow: in vivo feasibility of generalized phase-contrast MRI. J Magn Reson Imaging. 2008 Sep;28(3):655-63. doi: 10.1002/jmri.21475. PMID: 18777557. [doi] [pmid]

4. Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007;58(6):1182-1195. https://doi:10.1002/mrm.21391 [doi]

5. Vishnevskiy, V., Walheim, J. and Kozerke, S., Deep variational network for rapid 4D flow MRI reconstruction. Nature Machine Intelligence, 2020; 2(4), pp.228-235. https://doi.org/10.1038/s42256-020-0165-6 [doi]

6. Qazi, S.A., Khalid H, Viola F., et al., Accurate Peak and Mean Velocity with Deep Learning-Reconstructed highly Undersampled 4D Flow MRI using FlowVN, In the Proceedings of the annual meeting of ISMRM, Singapore, 2024.

7. Uecker M, Ong F, Tamir J, et al. Berkeley Advanced Reconstruction Toolbox. 2015.

8. Winkelmann, S., Schaeffter, T., Koehler, T., et al., An optimal radial profile order based on the Golden Ratio for time-resolved MRI. IEEE transactions on medical imaging, 2006; 26(1), pp.68-76. https://doi.org/10.1109/tmi.2006.885337 [doi]

9. Uecker, M., Lai, P., Murphy, M.J., Virtue, P., Elad, M., Pauly, J.M., Vasanawala, S.S. and Lustig, M., ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magnetic resonance in medicine, 2014; 71(3), pp.990-1001. https://doi.org/10.1002/mrm.24751 [doi]

10. Bustamante, M., Viola, F., Engvall, J., et al., Automatic time‐resolved cardiovascular segmentation of 4D flow MRI using deep learning. Journal of Magnetic Resonance Imaging, 2023; 57(1), pp.191-203. https://doi.org/10.1002/jmri.28221 [doi]

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http://echo.ismrm.org/p/ISMRM2026/465-02-004