Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
403-02-004 ISMRM Abstract

Synthesising Quantitative Susceptibility Maps from Multi-Parametric Maps (MPM2QSM)

Accepted
Mitchel Lee 1, Fenella Kirkham2, Karin Shmueli1
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
2UCL Great Ormond Street Institute of Child Health, London, United Kingdom
Presenting Author: Mitchel Lee

Synopsis

Motivation:
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References

1. Weiskopf, N. et al. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. Front. Neurosci. 7, (2013).
2. Murdoch, R., Kawadler, J., Carmichael, D., Kirkham, F. & Shmueli, K. Can Multi-Parametric Mapping Sequences Be Used for Accurate Quantitative Susceptibility Mapping? in International Society for Magnetic Resonance in Medicine (ISMRM). 3206 (2020).
3. Shmueli, K. Chapter 31 - Quantitative Susceptibility Mapping. in Quantitative Magnetic Resonance Imaging (eds. Seiberlich, N. et al.) vol. 1 819–838 (Academic Press, 2020).
4. Rosen, C. L. et al. Obstructive sleep apnea and sickle cell anemia. Pediatrics 134, 273–281 (2014).
5. Tabelow, K. et al. hMRI – A toolbox for quantitative MRI in neuroscience and clinical research. NeuroImage 194, 191–210 (2019).
6. Liu, T. et al. Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping. Magn. Reson. Med. 69, 467–476 (2013).
7. Dymerska, B. et al. Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO). Magn. Reson. Med. 85, 2294–2308 (2021).
8. Wu, B., Li, W., Guidon, A. & Liu, C. Whole brain susceptibility mapping using compressed sensing. Magn. Reson. Med. 67, 137–147 (2012).
9. Karsa, A., Punwani, S. & Shmueli, K. An optimized and highly repeatable MRI acquisition and processing pipeline for quantitative susceptibility mapping in the head-and-neck region. Magn. Reson. Med. 84, 3206–3222 (2020).
10. Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001).
11. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825–841 (2002).
12. Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma. 9351, 234–241 (2015).
13. Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J. & Maier-Hein, K. H. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203–211 (2021).
14. Longuefosse, A. et al. Adapted nnU-Net: A Robust Baseline for Cross-Modality Synthesis and Medical Image Inpainting. in Simulation and Synthesis in Medical Imaging (eds. Fernandez, V. et al.) 24–33 (Springer Nature Switzerland, Cham, 2025). doi:10.1007/978-3-031-73281-2_3. [doi]
15. Yoon, J. et al. Quantitative susceptibility mapping using deep neural network: QSMnet. NeuroImage 179, 199–206 (2018).
16. Milovic, C., Tejos, C., Silva, J., Shmueli, K. & Irarrazaval, P. XSIM: A structural similarity index measure optimized for MRI QSM. Magn. Reson. Med. 93, 411–421 (2025).
17. Zhang, J. et al. Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction. NeuroImage 211, 116579 (2020).
18. Oh, G., Bae, H., Ahn, H.-S., Park, S.-H. & Ye, J. C. CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN. Preprint at https://doi.org/10.48550/arXiv.2012.03842 (2020). [doi]

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