Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
631-02-006 / 631-02-006 ISMRM Abstract

Paramagnetic-to-diamagnetic susceptibility ratio as an imaging marker to detect glioblastoma infiltration

Primary: Neuro - Tumors
Secondary: Contrast Mechanisms - Susceptibility/QSM
631-02-006 · Everything About Contrast Mechanism · Thursday, 14 May, 1:40 PM–3:16 PM · Roof Terrace
631-02-006 · Everything About Contrast Mechanism · Thursday, 14 May, 1:40 PM–3:16 PM · Roof Terrace
Keywords: Infiltration Neurosurgery Glioblastoma Imaging biomarkers Quantitative Susceptibility Mapping (QSM)
Accepted
Giulia Debiasi 1,2,3, Giovanni Librizzi4,5, Valentina Visani2, Marco Castellaro2, Zhenghao Li6, Hongjiang Wei6, Renzo Manara4,5,7, Alessandra Bertoldo2,4, Chunlei Liu8,9
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States of America
2Department of Information Engineering, University of Padova, Padova, Italy
3Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy
4Padova Neuroscience Center, University of Padova, Padova, Italy
5Neuroradiology, Department of Neurosciences, University of Padova, Padova, Italy
6School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
7Department of Medicine, University of Padova, Padova, Italy
8Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States of America
9Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
Presenting Author: Giulia Debiasi

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References

1. Louis D, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-oncology. 08/02/2021 2021;23(8)doi:10.1093/neuonc/noab106 [doi]
2. Lasocki, A. & Gaillard, F. Non-Contrast-Enhancing Tumor: A New Frontier in Glioblastoma Research. AJNR. American journal of neuroradiology 40 (2019). https://doi.org/10.3174/ajnr.A6025 [doi]
3. Isensee, F., Jaeger, P., Kohl, S., Petersen, J. & Maier-Hein, K. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18 (2021). https://doi.org/10.1038/s41592-020-01008-z [doi]
4. Pemberton, H. et al. Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms. Scientific reports 13 (2023). https://doi.org/10.1038/s41598-023-44794-0 [doi]
5. Li W, Wu B, Liu C. STI Suite: a Software Package for Quantitative Susceptibility Imaging. Proc. Intl. Soc. Mag. Reson. Med. 22 (2014), Abstract 3265. https://chunleiliulab.github.io/software/#STI
6. Li W, Wu B, Liu C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. NeuroImage. 04/15/2011 2011;55(4)doi:10.1016/j.neuroimage.2010.11.088 [doi]
7. Li, W., Avram, A., Wu, B., Xiao, X. & Liu, C. Integrated Laplacian-based phase unwrapping and background phase removal for quantitative susceptibility mapping. NMR in biomedicine 27 (2014). https://doi.org/10.1002/nbm.3056 [doi]
8. Wu, B., Li, W., Guidon, A. & Liu, C. Whole brain susceptibility mapping using compressed sensing. Magnetic resonance in medicine 67 (2012). https://doi.org/10.1002/mrm.23000 [doi]
9. Wei, H. et al. Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range. NMR in biomedicine 28 (2015). https://doi.org/10.1002/nbm.3383 [doi]
10. Chen J, Gong N, Chaim K, Otaduy M, Liu C. Decompose quantitative susceptibility mapping (QSM) to sub-voxel diamagnetic and paramagnetic components based on gradient-echo MRI data. NeuroImage. 11/15/2021 2021;242doi:10.1016/j.neuroimage.2021.118477 [doi]

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http://echo.ismrm.org/p/ISMRM2026/631-02-006