A Comparative Evaluation of 7T MRI for Epilepsy with Deep-Learning–Based Image Reconstruction and Dynamic Parallel Transmission
Integration of dynamic parallel transmission and deep-learning-based reconstructions enhances image resolution, reduces scan time, and improves image homogeneity, addressing barriers to routine clinical implementation of 7T MRI. These advancements may improve lesion conspicuity and contribute to better outcomes for patients with epilepsy.
Authors
- Patrick Alexander Liebig (ORCID: https://orcid.org/0000-0001-7342-3715)
- Erik H. Middlebrooks (ORCID: https://orcid.org/0000-0002-4418-9605)
- Gian Franco Piredda (ORCID: https://orcid.org/0000-0002-8449-6634)
- Justyna O. Ekert (ORCID: https://orcid.org/0000-0002-4202-5746)
- Jürgen Herrler (ORCID: https://orcid.org/0000-0002-4620-8216)
- Shengzhen Tao (ORCID: https://orcid.org/0000-0003-3019-3491)
- John V. Murray (ORCID: https://orcid.org/0000-0002-1951-5608)
- Dominik Nickel
- Xiangzhi Zhou
- Erin M. Westerhold
- Vishal N. Patel
- Thomas Yu
- Vivek Kumar Gupta
Institutions
- École Polytechnique Fédérale de Lausanne (CH)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2026-02-10
- DOI
- https://doi.org/10.3174/ajnr.a9218
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00