SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation.
An SNRAware training scheme was proposed to leverage information from the MRI reconstruction process in deep learning denoising training, resulting in improved performance and good generalization properties.
Authors
- James Moon (ORCID: https://orcid.org/0000-0001-8071-1491)
- Iain Pierce (ORCID: https://orcid.org/0000-0001-8546-5023)
- Peter Kellman (ORCID: https://orcid.org/0000-0002-9875-6070)
- Thomas A. Treibel (ORCID: https://orcid.org/0000-0003-1560-7414)
- Charlotte Manisty (ORCID: https://orcid.org/0000-0003-0245-7090)
- Joseph Naegele
- Hui Xue (ORCID: https://orcid.org/0000-0002-4561-5530)
- Adrienne E. Campbell‐Washburn (ORCID: https://orcid.org/0000-0002-7169-5693)
- Sarah Hooper (ORCID: https://orcid.org/0000-0003-1110-0093)
- Rhodri Davies (ORCID: https://orcid.org/0000-0001-7630-7517)
- Michael S. Hansen (ORCID: https://orcid.org/0000-0002-8087-8731)
- John Stairs
Institutions
- National Institutes of Health (US)
- Barts Health NHS Trust (GB)
- National Heart, Lung, and Blood Institute (US)
- University College London (GB)
Publication Details
- Journal
- PubMed
- Published
- 2025-03-23
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00