Noise2Recon: Enabling SNR‐robust MRI reconstruction with semi‐supervised and self‐supervised learning
Abstract
To develop a method for building MRI reconstruction neural networks robust to changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled scans.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 10, 2023
- Source ID
- 10.1002/mrm.29759
Entities
People
- Akshay Chaudhari
- Arjun D Desai
- Batu M. Ozturkler
- Brian A. Hargreaves
- Christopher M Sandino
- Christopher Ré
- John M. Pauly
- Marc Willis
- Robert Boutin
- Shreyas Vasanawala
Organizations
- GE HealthCare
- Koninklijke Philips NV
- National Institute of Arthritis and Musculoskeletal and Skin Diseases
- National Institute of Biomedical Imaging and Bioengineering
- National Science Foundation of Sri Lanka
- Stanford University
- United States Department of Defense