Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
563-03-007 ISMRM Abstract

Characterisation of brain tumour type using multi-parametric information from GE-SE EPIK

Primary: Contrast Mechanisms - Multi-Contrast
Secondary: Neuro - Tumors
563-03-007 · Image Classification for Brain Cancer · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row D
Keywords: Multi-parametric MRI GE-SE EPIK Brain Tumor classification FET-PET
Accepted
Fabian Küppers 1, Keith George Ciantar, Seong Dae Yun1, Gabriele Stoffels1,2, Christian Filß1,2, Norbert Galldiks3,4,5, Felix M Mottaghy2,6, M. Eline Kooi6,7, Karl-Josef Langen1,2, Philipp Lohmann1, N. Jon Shah1,8,9,10
1Institute of Neuroscience and Medicine 4, Forschungszentrum Juelich, Juelich, Germany
2Department of Nuclear Medicine, RWTH Aachen University Hospital, Aachen, Germany
3Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
4Institute of Neuroscience and Medicine 3, Forschungszentrum Juelich, Juelich, Germany
5Center of Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Germany
6Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
7Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, Netherlands
8Institute of Neuroscience and Medicine 11, JARA, Forschungszentrum Juelich, Juelich, Germany
9JARA - BRAIN - Translational Medicine, Aachen, Germany
10Department of Neurology, RWTH Aachen University Hospital, Aachen, Germany
Presenting Author: Fabian Küppers

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References

1. H. U. Ahmed et al., “Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study,” The Lancet, vol. 389, no. 10071, pp. 815–822, Feb. 2017, doi: 10.1016/S0140-6736(16)32401-1. [doi]
2. F. Küppers, S. D. Yun, and N. J. Shah, “Development of a novel 10-echo multi-contrast sequence based on EPIK to deliver simultaneous quantification of T2 and T2* with application to oxygen extraction fraction,” Magn. Reson. Med., vol. 88, no. 4, pp. 1608–1623, Oct. 2022, doi: 10.1002/mrm.29305. [doi]
3. F. Küppers et al., “Brain Tumor Characterization Using Multiple MR Parameters From Multi-Contrast EPI With Keyhole (GE-SE EPIK) Including Oxygen Extraction Fraction: A Comparison to O-(2-[18F]Fluoroethyl)-L-Tyrosine (FET) Positron Emission Tomography,” J. Magn. Reson. Imaging JMRI, vol. 62, no. 3, pp. 721–736, Sept. 2025, doi: 10.1002/jmri.29795. [doi]
4. L. Caldeira et al., “The Jülich Experience With Simultaneous 3T MR-BrainPET: Methods and Technology,” IEEE Trans. Radiat. Plasma Med. Sci., vol. 3, no. 3, pp. 352–362, May 2019, doi: 10.1109/TRPMS.2018.2863953. [doi]
5. R. Gutsche et al., “Automated Brain Tumor Detection and Segmentation for Treatment Response Assessment Using Amino Acid PET,” J. Nucl. Med., vol. 64, no. 10, pp. 1594–1602, Oct. 2023, doi: 10.2967/jnumed.123.265725. [doi]
6. F. Isensee et al., “Automated brain extraction of multisequence MRI using artificial neural networks,” Hum. Brain Mapp., vol. 40, no. 17, pp. 4952–4964, 2019, doi: 10.1002/hbm.24750. [doi]
7. K. Ciantar et al., “JS01.3.A FULLY AUTOMATED EVALUATION OF FET PET IN BRAIN TUMOR PATIENTS - AN INTERNATIONAL, MULTICENTER STUDY,” Neuro-Oncol., vol. 27, no. Supplement_3, p. iii1, Oct. 2025, doi: 10.1093/neuonc/noaf193.001. [doi]

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http://echo.ismrm.org/p/ISMRM2026/563-03-007