Evaluating simultaneously derived T2, T2*, vCBV and OEF from GE-SE EPIK in brain tumour patients
Motivation: Multi-contrast GE/SE acquisitions provide versatile, simultaneously acquired MR parameters, with fast, non-invasive OEF quantification proving potentially exceptionally valuable in clinical practice. Goal(s): The tumour characterisation capability of 10-echo GE-SE EPIK-derived MR parameters was evaluated in standardised tumour VOIs compared to healthy brain tissue. Approach: A full-brain 2-minute protocol was acquired from 18 tumour patients during hybrid 3T MR and FET-PET acquisitions. Quantified MR parameters were analyzed in tumour VOIs defined by FET-PET. Results: The increased T2/T2* values and decreased vCBV/OEF obtained using 2-minute GE-SE EPIK acquisitions were in agreement with literature values. Impact: Whole-brain T2, T2*, vCBV, and OEF quantification can be obtained within a 2-minute TA using 10-echo GE-SE EPIK MRI, revealing changes in tumour VOIs in agreement with literature values, potentially providing fast clinical access to MR parameters, especially OEF.
Authors
- Fabian Küppers (ORCID: https://orcid.org/0000-0002-8365-6931)
- Seong Dae Yun (ORCID: https://orcid.org/0000-0001-7398-1899)
- M. Eline Kooi (ORCID: https://orcid.org/0000-0001-7562-5724)
- Mohamed Kassem (ORCID: https://orcid.org/0000-0001-8392-2323)
- Felix Manuel Mottaghy (ORCID: https://orcid.org/0000-0002-7212-6521)
- Karl‐Josef Langen (ORCID: https://orcid.org/0000-0003-1101-5075)
- Christian Filß (ORCID: https://orcid.org/0000-0002-9927-6335)
- Gabriele Stoffels (ORCID: https://orcid.org/0000-0001-7114-1941)
- N. Jon Shah (ORCID: https://orcid.org/0000-0002-8151-6169)
- Philipp Lohmann (ORCID: https://orcid.org/0000-0002-5360-046X)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/1308
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00