Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction
This study demonstrates the clinical feasibility of DLR in MR imaging of IDH-mutant gliomas, with significant time savings of 29.6 % on average and non-inferior image quality to CR. DLR sequences received strong multidisciplinary preference, underscoring their potential for enhancing neuro-oncological decision-making and suitability for clinical implementation.
Authors
- Georg Gohla (ORCID: https://orcid.org/0000-0001-7596-5139)
- Constantin Roder (ORCID: https://orcid.org/0000-0003-4487-2807)
- Till‐Karsten Hauser (ORCID: https://orcid.org/0000-0001-7604-3419)
- Christer Ruff (ORCID: https://orcid.org/0000-0002-9193-6116)
- Leonie Zerweck (ORCID: https://orcid.org/0000-0003-4105-5131)
- Frank Paulsen (ORCID: https://orcid.org/0000-0001-8836-528X)
- Paula Bombach (ORCID: https://orcid.org/0000-0002-7767-7824)
- Christoph Artzner (ORCID: https://orcid.org/0000-0002-7608-1762)
- Ulrike Ernemann (ORCID: https://orcid.org/0000-0003-1330-1235)
- Eliane Weinbrenner
Institutions
- Hertie Institute for Clinical Brain Research (DE)
- STZ eyetrial (DE)
- Diakonie-Klinikum Stuttgart (DE)
- University of Tübingen (DE)
Publication Details
- Journal
- European Journal of Radiology Open
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1016/j.ejro.2024.100617
- Citations
- 2
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.88