562-03-005 ISMRM Abstract

Pre-contrast 3D-MAGIC Multiparametric Mapping in Contrast-Enhancing Gliomas and Brain Metastases

Primary: Analysis Methods - Segmentation and Detection

Secondary: Neuro - Tumors

562-03-005 · Machine Learning in Neuroimaging · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row C

Keywords: Contrast-enhancement Contrast enhanced MRI Multiparametric quantitative MRI Contrast-agent free

Accepted

Noemi Sgambelluri ¹, Dirk H. J. Poot¹, Frans M Vos², Gonzalo E Mosquera Rojas¹, Juancito van Leeuwen¹, Florian Wiesinger^3,4, Marion Smits^1,5,6, juan hernandez tamames^1,2,5

¹Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands

²Imaging Physics, TU Delft, Delft, Netherlands

³GE Healthcare, Munich, Germany

⁴Centre for Neuroimaging Sciences, King's College London, London, United Kingdom

⁵Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands

⁶Medical Delta, Delft, Netherlands

Presenting Author: Noemi Sgambelluri

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.

References

1. M. R. Long, N. A. Dayan, M. Xu, W. Liao, A. A. Gilad, and M. C. DeLano, “Gadolinium Pollution—A Future Forward Perspective on Human and Environmental Impact,” Magnetic Resonance in Medicine (2025): 1–11, https://doi.org/10.1002/mrm.70157. [doi]

2. Warntjes, J. B. M., et al. "Rapid magnetic resonance quantification on the brain: optimization for clinical usage." Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 60.2 (2008): 320-329.

3. G Mosquera Rojas, J van Leeuwen, I Wamelink, V Keil, M Smits, S Klein, S van der Voort, P04.18.A GLIOSEG: AN INTEGRATED FRAMEWORK FOR ROBUST AI-BASED GLIOMA SEGMENTATION THROUGH MODEL ENSEMBLING, Neuro-Oncology, Volume 27, Issue Supplement_3, October 2025, Page iii63, https://doi.org/10.1093/neuonc/noaf193.204. [doi]

4. J van Leeuwen, G Mosquera Rojas, , I Wamelink, V Keil, M Smits, S Klein, “Glioseg: an integrated framework for robust AI-based glioma segmentation”, Book of Abstracts ESMRMB 2025 Online 41st Annual Scientific Meeting 8–11 October 2025. Magn Reson Mater Phy (2025). https://doi.org/10.1007/s10334-025-01278-8. [doi]

5. SPM25 – Statistical Parametric Mapping. https://www.fil.ion.ucl.ac.uk/spm/

6. Ashburner, John, and Karl J. Friston. "Unified segmentation." neuroimage 26.3 (2005): 839-851.

7. SyntheticMR AB. SyMRI software, version 12.0. Linköping, Sweden: SyntheticMR AB; 2024. Available at: https://www.syntheticmr.com

8. M. Jenkinson and S.M. Smith. A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2):143-156, 2001.

9. M. Jenkinson, P.R. Bannister, J.M. Brady, and S.M. Smith. Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2):825-841, 2002.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/562-03-005