Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
567-02-008 ISMRM Abstract

A Vendor-Agnostic Pulseq Implementation of MR-STAT for fast, high resolution quantitative MRI

Primary: Acquisition & Reconstruction - Open source software, sequences, and reconstruction algorithms
Secondary: Acquisition & Reconstruction - Pulse Sequence Design and Spatial Encoding Methods
567-02-008 · Open-Source Software, Sequences, and Reconstruction Algorithms · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row H
Keywords: Quantitative Imaging Open-source software MR-STAT Pulseq
Accepted
Oscar van der Heide 1, Edwin Versteeg1, Joost Kuijer2, Martin B Schilder1, Christoph Kolbitsch3, Vera C Keil2, Cornelis A van den Berg1, Alessandro A Sbrizzi1
1Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
2Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
3Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Berlin, Germany
Presenting Author: Oscar van der Heide

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References

1. Sbrizzi, Alessandro et al. “Fast quantitative MRI as a nonlinear tomography problem.” Magnetic resonance imaging vol. 46 (2018): 56-63. doi:10.1016/j.mri.2017.10.015 [doi]
2. van der Heide, Oscar et al. “High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm.” NMR in biomedicine vol. 33,4 (2020): e4251. doi:10.1002/nbm.4251 [doi]
3. Layton, Kelvin J et al. “Pulseq: A rapid and hardware-independent pulse sequence prototyping framework.” Magnetic resonance in medicine vol. 77,4 (2017): 1544-1552. doi:10.1002/mrm.26235 [doi]
4. Liu, Hongyan et al. “Time-efficient, high-resolution 3T whole-brain relaxometry using Cartesian 3D MR Spin TomogrAphy in Time-Domain (MR-STAT) with cerebrospinal fluid suppression.” Magnetic resonance in medicine vol. 93,5 (2025): 2008-2019. doi:10.1002/mrm.30384 [doi]
5. Ravi et al., (2019). PyPulseq: A Python Package for MRI Pulse Sequence Design. Journal of Open Source Software, 4(42), 1725, https://doi.org/10.21105/joss.01725 [doi]
6. van der Heide, Oscar et al. “GPU-accelerated Bloch simulations and MR-STAT reconstructions using the Julia programming language.” Magnetic resonance in medicine vol. 92,2 (2024): 618-630. doi:10.1002/mrm.30074 [doi]
7. Coupé, Pierrick et al. “AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation.” NeuroImage vol. 219 (2020): 117026. doi:10.1016/j.neuroimage.2020.117026 [doi]
8. Jenkinson, Mark et al. “FSL.” NeuroImage vol. 62,2 (2012): 782-90. doi:10.1016/j.neuroimage.2011.09.015 [doi]
9. Bojorquez, Jorge Zavala et al. “What are normal relaxation times of tissues at 3 T?.” Magnetic resonance imaging vol. 35 (2017): 69-80. doi:10.1016/j.mri.2016.08.021 [doi]
10. Ma, Dan et al. “Slice profile and B1 corrections in 2D magnetic resonance fingerprinting.” Magnetic resonance in medicine vol. 78,5 (2017): 1781-1789. doi:10.1002/mrm.26580 [doi]

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http://echo.ismrm.org/p/ISMRM2026/567-02-008