Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
630-01-004 ISMRM Abstract

Finite-difference based MR Electrical Properties Reconstruction Optimization Method at 3T

Accepted
Kyu-Jin Jung 1,2, Thierry Meerbothe3,4, Chuanjiang Cui2, Changmin Ryu2, Mina Park5, Cornelis A van den Berg3,4, Stefano Mandija3,4, Chunlei Liu1,6, Dong-Hyun Kim2
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States of America
2Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of
3Department of Radiotherapy, UMC Utrecht, Utrecht, Netherlands
4Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, UMC Utrecht, Utrecht, Netherlands
5Gangnam Severance Hospital, Seoul, Korea, Republic of
6Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
Presenting Author: Kyu-Jin Jung

Synopsis

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References

1. Katscher U, Kim D-H, Seo JK. Recent progress and future challenges in MR electric properties tomography. Computational and mathematical methods in medicine. 2013;2013(1):546562. https://doi.org/10.1155/2013/546562 [doi]
2. Katscher U, et al. Estimation of breast tumor conductivity using parabolic phase fitting. In: Proceedings 2012 ISMRM Annual Meeting. 2012;p.3482.
3. Lee SK, Bulumulla S, Hancu I. Theoretical investigation of random noise limited signal-to-noise ratio in MR-based electrical properties tomography. IEEE Trans. Med. Imag. 2015;34(11):2220–2232. https://doi.org/10.1109/TMI.2015.2427236. [doi]
4. Gurler N & Ider YZ. Gradient-based electrical conductivity imaging using MR phase. Magn. Reson. Med. 2017;77(1):137–150. https://doi.org/10.1002/mrm.26097. [doi]
5. Liu J, et al. Electrical properties tomography based on B1 maps in MRI: principles, applications, and challenges. IEEE Trans Biomed Eng. 2017;64:2515-2530. https://doi.org/10.1109/TBME.2017.2725140. [doi]
6. Karsa A & Shmueli K. New approaches for simultaneous noise suppression and edge preservation to achieve accurate quantitative conductivity mapping in noisy images. In: Proceedings 2021 ISMRM Annual Meeting. 2021;p.3774.
7. Zilberti L, et al. Magnetic resonance-based electric properties tomography via Green’s integral identity. IEEE Access, 2025;13:42029-42044. https://doi.org/10.1109/ACCESS.2025.3546036. [doi]
8. Mandija S, et al. Error analysis of helmholtz-based MR-electrical properties tomography. Magn. Reson. Med. 2018;80(1),90–100. https://doi.org/10.1002/mrm.27004. [doi]
9. Karsa A, Fuchs P, Shmueli K. Optimal kernel radii for calculating the derivatives of noisy B1 phase for accurate phase-based quantitative conductivity mapping. In: Proceedings 2021 ISMRM Annual Meeting. 2021;p.3775.
10. Meerbothe TG, et al. Electrical properties based B1+ prediction for electrical properties tomography reconstruction evaluation. Magn. Reson. Med. 2025;94(3):1269-1283. https://doi.org/10.1002/mrm.30520. [doi]
11. Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774-781. https://doi.org/10.1016/j.neuroimage.2012.01.021. [doi]
12. Meerbothe TG, et al. ADEPT: A Database for MR-based Electrical Properties Tomography. Magn. Reson. Med. 2024;91(3):1190-1199. https://doi.org/10.1002/mrm.29904. [doi]
13. Mandija S, et al. First MR Electrical Properties Tomography reconstruction challenge: phase 3 - conductivity reconstructions from measured data. In: Proceedings 2025 ISMRM Annual Meeting. 2025;p.3425.
14. Choi YG, et al. An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using graphics processing units. Computational Statistics, 2022;37(1):419-443. doi.org/10.1007/s00180-021-01127-x. [doi]
15. Tyurin A and Richtarik P. Optimal time complexities of parallel stochastic optimization methods under a fixed computation model. Advances in Neural Information Processing Systems, 2023,36: 16515-16577.
16. Jung KJ, et al. Data-driven electrical conductivity brain imaging using 3 T MRI. Human Brain Mapping. 2023 ;44(15):4986-5001. https://doi.org/10.1002/hbm.26421. [doi]
17. Ruan G, et al. Magnetic resonance electrical properties tomography based on modified physics-informed neural network and multiconstraints. IEEE Transactions on Medical Imaging. 2024 ;43(9):3263-3278. https://doi.org/10.1109/TMI.2024.3391651. [doi]
18. Giannakopoulos II, et al. MR electrical properties mapping using vision transformers and canny edge detectors. Magnetic resonance in medicine. 2025;93(3):1117-1131. https://doi.org/10.1002/mrm.30338. [doi]
19. Jung KJ & Meerbothe TG, et al. A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography. Neuroimage. 2025;15(307):121054. https://doi.org/10.1016/j.neuroimage.2025.121054. [doi]

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