569-01-006 ISMRM Abstract

A High-Performance Python EPG Implementation for 3D MRF using GPU Acceleration with Triton

Primary: Acquisition & Reconstruction - Signal Modeling

Secondary: Analysis Methods - Software Tools

569-01-006 · Signal Modeling and Signal Preparation: Novel Approaches · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row J

Keywords: Extended Phase Graph GPU acceleration Python Simulation

Accepted

Gabriel Zihlmann ¹, Najat Salameh¹, Mathieu Sarracanie¹

¹Center for Adaptable MRI Technology (AMT Center), Institute of Medical Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom

Presenting Author: Gabriel Zihlmann

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.

References

1. Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495(7440):187-192. doi:10.1038/nature11971 [doi]

2. Weigel M. Extended phase graphs: Dephasing, RF pulses, and echoes - pure and simple. J Magn Reson Imaging. 2015;41(2):266-295. doi:10.1002/jmri.24619 [doi]

3. Jiang Y, Ma D, Seiberlich N, Gulani V, Griswold MA. MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magn Reson Med. 2015;74(6):1621-1631. doi:10.1002/mrm.25559 [doi]

4. Jordan SP, Hu S, Rozada I, et al. Automated design of pulse sequences for magnetic resonance fingerprinting using physics-inspired optimization. Proc Natl Acad Sci. 2021;118(40):e2020516118. doi:10.1073/pnas.2020516118 [doi]

5. Wang D, Ostenson J, Smith DS. snapMRF: GPU-accelerated magnetic resonance fingerprinting dictionary generation and matching using extended phase graphs. Magn Reson Imaging. 2020;66:248-256. doi:10.1016/j.mri.2019.11.015 [doi]

6. van der Heide O, van den Berg CAT, Sbrizzi A. GPU-accelerated Bloch simulations and MR-STAT reconstructions using the Julia programming language. Magn Reson Med. 2024;92(2):618-630. doi:10.1002/mrm.30074 [doi]

7. Most used languages among software developers globally 2024. Statista. https://www.statista.com/statistics/793628/worldwide-developer-survey-most-used-languages/. Published 2024. Accessed October 29, 2025.

8. Lee PK, Watkins LE, Anderson TI, Buonincontri G, Hargreaves BA. Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations. Magn Reson Med. 2019;82(4):1438-1451. doi:10.1002/mrm.27832 [doi]

9. Tillet P, Kung HT, Cox D. Triton: an intermediate language and compiler for tiled neural network computations. In: Proceedings of the 3rd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages. MAPL 2019. New York, NY, USA: Association for Computing Machinery; 2019:10-19. doi:10.1145/3315508.3329973 [doi]

10. Brian Hargreaves. Extended Phase Graph functions and examples. January 2012. https://web.stanford.edu/~bah/software/epg/. Accessed May 9, 2025.

11. Hennig J, Scheffler K. Hyperechoes. Magn Reson Med. 2001;46(1):6-12. doi:10.1002/mrm.1153 [doi]

12. Graf C, Rund A, Aigner CS, Stollberger R. Accuracy and performance analysis for Bloch and Bloch-McConnell simulation methods. J Magn Reson. 2021;329:107011. doi:10.1016/j.jmr.2021.107011 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/569-01-006