Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
351-03-012 / 351-03-012 ISMRM Abstract

Automated Sequence Design using Neural Architecture Search: In-depth Exploration on Simulation Fidelity

Primary: Acquisition & Reconstruction - AI methods
Secondary: Acquisition & Reconstruction - Pulse Sequence Design and Spatial Encoding Methods
351-03-012 · AI Methods · Monday, 11 May, 4:10 PM–5:46 PM · Power Pitch Theatre 1
351-03-012 · AI Methods · Monday, 11 May, 4:10 PM–5:46 PM · Power Pitch Theatre 1
Keywords: Pulse Sequence Design Sequence parameters optimization Sequence development MRI sequence optimization
Accepted
Rokgi Hong 1, Hongjun An1, Sooyeon Ji2, Taechang Kim1, Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of
2Division of Computer Engineering, Hankuk university of Foreign Studies, gyeonggi-do, Korea, Republic of
Presenting Author: Rokgi Hong

Synopsis

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References

1. A. Loktyushin, K. Herz, N. Dang, F. Glang, A. Deshmane, S. Weinmüller, A. Doerfler, B. Schölkopf, K. Scheffler, and M. Zaiss, “MRzero ‐ Automated discovery of MRI sequences using supervised learning,” Magn. Reson. Med., vol. 86, no. 2, pp. 709–724, 2021. doi:10.1002/mrm.28727 [doi]
2. O. Perlman, B. Zhu, M. Zaiss, M. S. Rosen, and C. T. Farrar, “An end-to-end AI-based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST),” Magn. Reson. Med., vol. 87, no. 6, pp. 2792–2810, 2022. doi:10.1002/mrm.29173 [doi]
3. S. P. Jordan, S. Hu, I. Rozada, and D. Ma, “Automated design of pulse sequences for magnetic resonance fingerprinting using physics-inspired optimization,” Proc. Natl. Acad. Sci. U. S. A., vol. 118, no. 40, e2020516118, 2021. doi:10.1073/pnas.2020516118 [doi]
4. F. Glang, S. Mueller, K. Herz, A. Loktyushin, K. Scheffler, and M. Zaiss, “MR-double-zero – Proof-of-concept for a framework to autonomously discover MRI contrasts,” J. Magn. Reson., vol. 341, 107237, 2022. doi:10.1016/j.jmr.2022.107237 [doi]
5. B. Zhu, J. Liu, N. Koonjoo, B. R. Rosen, and M. S. Rosen, “AUTOmated pulse SEQuence generation (AUTOSEQ) using Bayesian reinforcement learning in an MRI physics simulation environment,” in Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2018.
6. S. Walker-Samuel, “Using deep reinforcement learning to actively, adaptively and autonomously control a simulated MRI scanner,” in Proceedings of the ISMRM 27th Annual Meeting & Exhibition, 2019.
7. H. An, S. Ji, D. Shin, S. Oh, J. Kim, and J. Lee, “Automated sequence design using neural architecture search,” in Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2022 & ISMRT Annual Meeting, 2022.
8. R. Hong, H. An, S. Ji, and J. Lee, “Automated sequence design using neural architecture search: reliability and reproducibility,” in Proceedings of the 2024 ISMRM & ISMRT Annual Meeting, 2024.
9. H. Cai, L. Zhu, and S. Han, “ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware,” in 7th International Conference on Learning Representations, 2019, pp. 1–13.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/351-03-012