Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
352-01-002 / 352-01-002 ISMRM Abstract

Fast and accurate Bloch simulations using Magnus expansions

Primary: Physics & Engineering - RF Pulse Design & Fields
Secondary: Acquisition & Reconstruction - Open source software, sequences, and reconstruction algorithms
352-01-002 · New Methods: Acquisition and Reconstruction · Monday, 11 May, 8:20 AM–9:56 AM · Power Pitch Theatre 2
352-01-002 · New Methods: Acquisition and Reconstruction · Monday, 11 May, 8:20 AM–9:56 AM · Power Pitch Theatre 2
Keywords: Open-source software RF Pulse Design Bloch simulations GPU acceleration
Accepted
Carlos A Castillo-Passi 1,2, Charles McGrath1,2, Kareem Fareed1, Jacob S Blum1,2, Daniel B Ennis1,3
1Department of Radiology, Stanford University, Stanford, United States of America
2Stanford Cardiovascular Institute, Stanford Medicine, Stanford, United States of America
3Department of Bioengineering, Stanford University, Stanford, United States of America
Presenting Author: Carlos A Castillo-Passi

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Combettes, P.L., Pesquet, J.-C., 2011. Proximal Splitting Methods in Signal Processing, in: Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Springer Optimization and Its Applications. Springer, New York, NY, pp. 185–212. https://doi.org/10.1007/978-1-4419-9569-8_10 [doi]
2. Graf, C., Rund, A., Aigner, C.S., Stollberger, R., 2021. Accuracy and performance analysis for Bloch and Bloch-McConnell simulation methods. Journal of Magnetic Resonance 329, 107011. https://doi.org/10.1016/j.jmr.2021.107011 [doi]
3. Weine, J., McGrath, C., Dirix, P., Buoso, S., Kozerke, S., 2024. CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow. Magnetic Resonance in Medicine 91, 2621–2637. https://doi.org/10.1002/mrm.30010 [doi]
4. Loktyushin, A., Herz, K., Dang, N., Glang, F., Deshmane, A., Weinmüller, S., Doerfler, A., Schölkopf, B., Scheffler, K., Zaiss, M., 2021. MRzero - Automated discovery of MRI sequences using supervised learning. Magnetic Resonance in Medicine 86, 709–724. https://doi.org/10.1002/mrm.28727 [doi]
5. Shinnar, M., Eleff, S., Subramanian, H., Leigh, J.S., 1989. The synthesis of pulse sequences yielding arbitrary magnetization vectors. Magn Reson Med 12, 74–80. https://doi.org/10.1002/mrm.1910120109 [doi]
6. Hochbruck, M., Lubich, C., 2003. On Magnus Integrators for Time-Dependent Schrödinger Equations. SIAM J. Numer. Anal. 41, 945–963. https://doi.org/10.1137/S0036142902403875 [doi]
7. Magnus, W., 1954. On the exponential solution of differential equations for a linear operator. Communications on Pure and Applied Mathematics 7, 649–673. https://doi.org/10.1002/cpa.3160070404 [doi]
8. Blanes, S., Casas, F., Oteo, J.A., Ros, J., 2010. A pedagogical approach to the Magnus expansion. Eur. J. Phys. 31, 907. https://doi.org/10.1088/0143-0807/31/4/020 [doi]
9. Gonzalez Riedel, S., 2021. Solving the Bloch equation with the Magnus expansion (Master Thesis).
10. Lauzon, M.L., 2020. A Beginner’s Guide to Bloch Equation Simulations of Magnetic Resonance Imaging Sequences. arXiv:2009.02789.
11. Castillo-Passi C, Coronado R, Varela-Mattatall G, Alberola-López C, Botnar R, Irarrazaval P. KomaMRI.jl: An open-source framework for general MRI simulations with GPU acceleration. Magn Reson Med. 2023; 90: 329-342. https://doi.org/10.1002/mrm.29635 [doi]
12. Villacorta-Aylagas, P., Castillo-Passi, C.A., Kierulf, R.A., Menchón-Lara, R.M., Rodríguez-Galván, J.R., Sierra-Pallares, J.B., Irarrazaval, P., Alberola-López, C., n.d. Versatile and Highly Efficient MRI Simulation of Arbitrary Motion in KomaMRI. Magnetic Resonance in Medicine n/a. https://doi.org/10.1002/mrm.70145 [doi]
13. Malitsky, Y., Mishchenko, K., 2020. Adaptive Gradient Descent without Descent. https://doi.org/10.48550/arXiv.1910.09529 [doi]
14. Glover, G.H., 1999. Simple analytic spiral K-space algorithm. Magn Reson Med 42, 412–415. https://doi.org/10.1002/(SICI)1522-2594(199908)42:2<412::AID-MRM25>3.0.CO;2-U [doi]
15. Layton, K.J., Kroboth, S., Jia, F., Littin, S., Yu, H., Leupold, J., Nielsen, J.-F., Stöcker, T., Zaitsev, M., 2017. Pulseq: A rapid and hardware-independent pulse sequence prototyping framework. Magnetic Resonance in Medicine 77, 1544–1552. https://doi.org/10.1002/mrm.26235 [doi]
16. Moses William, Churavy Valentin. Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients. https://doi.org/10.5555/3495724.3496770 [doi]
17. William S. Moses, Valentin Churavy, Ludger Paehler, Jan Hückelheim, Sri Hari Krishna Narayanan, Michel Schanen, and Johannes Doerfert. 2021. Reverse-mode automatic differentiation and optimization of GPU kernels via Enzyme. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '21). Association for Computing Machinery, New York, NY, USA, Article 61, 1–16. https://doi.org/10.1145/3458817.3476165 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/352-01-002