Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
568-05-014 ISMRM Abstract

A GPU Accelerated EPG simulator for Reinforcement Learning

Primary: Acquisition & Reconstruction - Open source software, sequences, and reconstruction algorithms
Secondary: Acquisition & Reconstruction - Quantitative Imaging: head and neck
568-05-014 · Image Reconstruction I · Wednesday, 13 May, 4:00 PM–4:55 PM · Digital Posters Row I
Keywords: MR Fingerprinting Software Tools Sequence parameters optimization GPU acceleration
Accepted
Shenjun Zhong 1,2, Zhifeng Chen3, Zhaolin Chen4,5
1Monash Biomedical Imaging, Monash University, Melbourne, Australia
2National Imaging Facility, Brisbane, Australia
3Neusoft Medical Systems Co., Ltd., Hangzhou, China
4Monash Biomedical Imaging, Monash University, Clayton, Victoria, Clayton, Australia
5Department of Data Science and AI, Monash University, Clayton, Victoria, Clayton, Australia
Presenting Author: Shenjun Zhong

Synopsis

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References

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2. Zhao, B., Haldar, J. P., Liao, C., Ma, D., Jiang, Y., Griswold, M. A., ... & Wald, L. L. (2018). Optimal experiment design for magnetic resonance fingerprinting: Cramér-Rao bound meets spin dynamics. IEEE transactions on medical imaging, 38(3), 844-861.
3. Assländer, J., Lattanzi, R., Sodickson, D. K., & Cloos, M. A. (2017). Relaxation in spherical coordinates: analysis and optimization of pseudo‐SSFP based MR‐fingerprinting. arXiv preprint arXiv:1703.00481.
4. Lee, P. K., Watkins, L. E., Anderson, T. I., Buonincontri, G., & Hargreaves, B. A. (2019). Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations. Magnetic resonance in medicine, 82(4), 1438-1451.
5. Slioussarenko, C., Baudin, P. Y., & Marty, B. (2025). A steady‐state MR fingerprinting sequence optimization framework applied to the fast 3D quantification of fat fraction and water T1 in the thigh muscles. Magnetic Resonance in Medicine, 93(6), 2623-2639.
6. Nurdinova, A., Ruschke, S., Gestrich, M., Stelter, J., & Karampinos, D. C. (2025). Gpu-accelerated JEMRIS for extensive MRI simulations. Magnetic Resonance Materials in Physics, Biology and Medicine, 38(4), 679-694.
7. Liu, F., Block, W. F., Kijowski, R., & Samsonov, A. (2016). MRiLab: Fast Realistic MRI Simulations Based on Generalized Exchange Tissue Model. IEEE Trans. Med Imaging.
8. Castillo‐Passi, C., Coronado, R., Varela‐Mattatall, G., Alberola‐López, C., Botnar, R., & Irarrazaval, P. (2023). KomaMRI. Jl: an open‐source framework for general MRI simulations with GPU acceleration. Magnetic resonance in medicine, 90(1), 329-342.
9. Bloch, F. (1946). Nuclear induction. Physical review, 70(7-8), 460.
10. Weigel, M. (2015). Extended phase graphs: dephasing, RF pulses, and echoes‐pure and simple. Journal of Magnetic Resonance Imaging, 41(2), 266-295.
11. Scheffler, K., & Lehnhardt, S. (2003). Principles and applications of balanced SSFP techniques. European radiology, 13(11), 2409-2418.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/568-05-014