Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-04-011 ISMRM Abstract

Synthetic Image and Multi-coil K-space Data Generation From Multi-parameter Maps

Primary: Analysis Methods - Image Synthesis and Translation
Secondary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI
360-04-011 · Quantitative Mapping: Acquisition and Reconstruction · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row A
Keywords: Analysis/Processing Modelling MR Fingerprinting/Synthetic MR Signal Representations Synthetic MRI (SyMRI)
Accepted
Boran Kilic1, Nikolaus Weiskopf1,2,3, Kerrin J Pine 1
1Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
2Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth System Sciences, Leipzig University, Leipzig, Germany
3Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
Presenting Author: Kerrin J Pine

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. Vaculčiaková L, Podranski K, Edwards LJ, et al. Combining navigator and optical prospective motion correction for high-quality 500 μm resolution quantitative multi-parameter mapping at 7T. Magn Reson Med. 2022; 88: 787-801. doi:10.1002/mrm.29253 [doi]
2. Billot B, Greve DN, Puonti O, et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Med Image Anal. 2023;88:102789. https://doi.org/10.1016/j.media.2023.102789 [doi]
3. Billot B, Greve DN, Puonti O, et al. SynthSR: A public AI tool for automatic MRI synthetic contrast generation, super-resolution, and bias field correction. NeuroImage. 2021;240:118343. https://doi.org/10.1016/j.neuroimage.2021.118343 [doi]
4. Liu F, Velikina JV, Block WF, et al. MRiLab: An integrated numerical MRI simulation package. Comput Methods Programs Biomed. 2021;106371. https://doi.org/10.1016/j.cmpb.2021.106371 [doi]
5. Stöcker, T., Vahedipour, K., Pflugfelder, D. and Shah, N.J. (2010), High-performance computing MRI simulations. Magn. Reson. Med., 64: 186-193. https://doi.org/10.1002/mrm.22406 [doi]
6. Aubert-Broche B, Evans AC, Collins DL. A new improved version of the realistic digital brain phantom. NeuroImage. 2006;32(1):138–145. (BrainWeb dataset)
7. Dong H, Zhang Y, Xie Y, et al. Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning. arXiv:2501.12057 [cs.CV]. https://doi.org/10.48550/arXiv.2501.12057 [doi]
8. Xu H, Cai Y, Li W, et al. UltimateSynth: MRI Physics for Pan-Contrast AI. bioRxiv. 2024;2024.12.05.627056. https://doi.org/10.1101/2024.12.05.627056 [doi]
9. Dinsdale NK, Jenkinson M, Namburete AIL. A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI. Front Neurosci. 2021;15:708196. https://doi.org/10.3389/fnins.2021.708196 [doi]
10. Tabelow et al. (2019). hMRI – A toolbox for quantitative MRI in neuroscience and clinical research. NeuroImage, 194, 191-210. https://doi.org/10.1016/j.neuroimage.2019.01.029. [doi]
11. Peretti, L.; Donatelli, G.; Cencini, M.; Cecchi, P.; Buonincontri, G.; Cosottini, M.; Tosetti, M.; Costagli, M. Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool. Tomography 2023, 9, 1723-1733. https://doi.org/10.3390/tomography9050137 [doi]
12. M. ESPIRiT — An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE Meets GRAPPA. Magnetic Resonance in Medicine. 2014 Mar;71(3):990-1001. doi:10.1002/mrm.24751 [doi]
13. Dymerska B, Eckstein K, Bachrata B, Siow B, Trattnig S, Shmueli K, Robinson SD. Phase Unwrapping with a Rapid Opensource Minimum Spanning TreE AlgOrithm (ROMEO). Magnetic Resonance in Medicine. 2021; published online ahead of print. doi:10.1002/mrm.28563 [doi]
14. Haldar JP. Low-rank modeling of local k-space neighborhoods (LORAKS) for constrained MRI. IEEE Trans Med Imaging. 2014 Mar;33(3):668-81. doi: 10.1109/TMI.2013.2293974. PMID: 24595341; PMCID: PMC4122573. [doi] [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/360-04-011