Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits
Patients with neurological deficits can undergo MRI at a range of moderate to high acceleration. DL reconstruction outperforms CS in terms of image resolution, efficient denoising with a modest reduction in contrast and reduced reconstruction times.
Authors
- Luka C. Liebrand (ORCID: https://orcid.org/0000-0001-9184-3190)
- Bart J. Emmer (ORCID: https://orcid.org/0000-0001-8462-4037)
- Henk A. Marquering (ORCID: https://orcid.org/0000-0002-1414-6313)
- Charles B.L.M. Majoie (ORCID: https://orcid.org/0000-0002-7600-9568)
- Émilie Poirion
- Stefan D. Roosendaal (ORCID: https://orcid.org/0000-0002-3763-4774)
- Matthan W.A. Caan (ORCID: https://orcid.org/0000-0002-5162-8880)
- Dimitrios Karkalousos (ORCID: https://orcid.org/0000-0001-5983-0322)
- Julien Savatovsky
Institutions
- Amsterdam Neuroscience (NL)
- Fondation de Rothschild (FR)
- Amsterdam University Medical Centers (NL)
- University of Amsterdam (NL)
Publication Details
- Journal
- Magnetic Resonance Materials in Physics Biology and Medicine
- Published
- 2024-08-30
- DOI
- https://doi.org/10.1007/s10334-024-01200-8
- Citations
- 2
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- Health~Holland