Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-04-013 ISMRM Abstract

Variational Reconstruction Networks Preserving Phase-cycled bSSFP Signal Properties for T1 and T2 Mapping in the Brain

Accepted
David Sapienza 1,2,3,4, Nicholas Simic1,2,4, Sam Ruysseveldt1,2,4, Berk C Acikgoz1,2,5, Li Feng6,7, Gabriele Bonanno1,8,9, Kelvin Chow10, Jessica Bastiaansen1,2, Eva S Peper1,2,4
1Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, Switzerland
2Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland
3Department of Mathematics, Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland
4Center for Artificial Intelligence in Medicine (CAIM), University of Bern, Bern, Switzerland
5Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
6Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
7Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
8Swiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland
9Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
10Cardiovascular MR R&D, Siemens Healthcare Ltd., Calgary, Canada
Presenting Author: David Sapienza

Synopsis

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References

1. Zur Y, Wood ML, Neuringer LJ. Motion-insensitive, steady-state free precession imaging. Magn Reson Med. 1990 Dec;16(3):444-59. doi: 10.1002/mrm.1910160311. [doi]
2. Ganter C. Steady state of gradient echo sequences with radiofrequency phase cycling: analytical solution, contrast enhancement with partial spoiling. Magn Reson Med. 2006 Jan;55(1):98-107. doi: 10.1002/mrm.20736. [doi]
3. Ganter C. Static susceptibility effects in balanced SSFP sequences. Magn Reson Med. 2006 Sep;56(3):687-91. doi: 10.1002/mrm.20986. [doi]
4. Shcherbakova Y, van den Berg CAT, Moonen CTW, Bartels LW. PLANET: An ellipse fitting approach for simultaneous T 1 and T 2 mapping using phase‐cycled balanced steady‐state free precession. Magn Reson Med. 2018;79(2):711-722. doi:10.1002/mrm.26717 [doi]
5. Plähn NMJ, Safarkhanlo Y, Açikgöz BC, et al. ORACLE: An analytical approach for T1, T2, proton density, and off‐resonance mapping with phase‐cycled balanced steady‐state free precession. Magn Reson Med. December 2024. doi:10.1002/mrm.30388 [doi]
6. Rossi GMC, Mackowiak ALC, Açikgöz BC, et al. SPARCQ: A new approach for fat fraction mapping using asymmetries in the phase‐cycled balanced SSFP signal profile. Magn Reson Med. 2023;90(6):2348-2361. doi:10.1002/mrm.29813 36. [doi]
7. Acikgoz BC, Sainz Martinez C, Mackowiak ALC, et al. Quantitative susceptibility mapping in the human brain at 7T with phase‐cycled balanced SSFP. Magn Reson Med. June 2025. doi:10.1002/mrm.30571 [doi]
8. 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. doi:10.1002/mrm.21391 [doi]
9. Cukur T. Accelerated phase-cycled SSFP imaging with compressed sensing. IEEE Trans Med Imaging. 2015 Jan;34(1):107-15. doi: 10.1109/TMI.2014.2346814. Epub 2014 Aug 12. PMID: 25134078. [doi] [pmid]
10. Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T, Knoll F. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med. 2018 Jun;79(6):3055-3071. doi: 10.1002/mrm.26977. [doi]
11. Sriram A, Zbontar J, Murrell T, et al. End-to-End Variational Networks for Accelerated MRI Reconstruction. In: ; 2020:64-73. doi:10.1007/978-3-030-59713-9_7 [doi]
12. Layton KJ, Kroboth S, Jia F, Littin S, Yu H, Leupold J, Nielsen JF, Stöcker T, Zaitsev M. Pulseq: A rapid and hardware-independent pulse sequence prototyping framework. Magn Reson Med. 2017 Apr;77(4):1544-1552. doi: 10.1002/mrm.26235. [doi]
13. Gilsing et al. Deep learning image reconstruction of accelerated 3D Cartesian Phase-Cycled bSSFP for quantitative mapping, Proc Int Soc Magn Reson Med 2025
14. Chow K, Kellman P, Xue H. Prototyping image reconstruction and analysis with FIRE. SCMR 24th Annual Scientific Sessions 2021.
15. Feng L, Wen Q, Huang C, Tong A, Liu F, Chandarana H. GRASP-Pro: imProving GRASP DCE-MRI through self-calibrating subspace-modeling and contrast phase automation. Magn Reson Med. 2020 doi:10.1002/mrm.27903 [doi]
16. Heule R, Bause J, Pusterla O, Scheffler K. Multi‐parametric artificial neural network fitting of phase‐cycled balanced steady‐state free precession data. Magn Reson Med. 2020;84(6):2981-2993. doi:10.1002/mrm.28325 [doi]

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