Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning
Synthetic raw data generation improved reconstruction quality in scenarios with limited training data. A major advantage of synthetic data generation is that it allows for the reuse of magnitude-only datasets, which are more readily available than raw datasets.
Authors
- Frank Zijlstra (ORCID: https://orcid.org/0000-0002-9184-7666)
- Peter T. While (ORCID: https://orcid.org/0000-0003-2602-0758)
Institutions
- Norwegian University of Science and Technology (NO)
- St Olav's University Hospital (NO)
Publication Details
- Journal
- Magnetic Resonance Materials in Physics Biology and Medicine
- Published
- 2024-08-29
- DOI
- https://doi.org/10.1007/s10334-024-01193-4
- Citations
- 3
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Norges Teknisk-Naturvitenskapelige Universitet
- Norges Forskningsråd
- St. Olavs Hospital Universitetssykehuset i Trondheim