Robust multi‐coil MRI reconstruction via self‐supervised denoising
We showed that denoising is an essential pre-processing technique capable of improving the efficacy of DL-based MRI reconstruction methods under diverse conditions. By refining the quality of input data, denoising enables training more effective DL networks, potentially bypassing the need for noise-free reference MRI scans.
Authors
- Marius Arvinte (ORCID: https://orcid.org/0000-0002-9328-9223)
- Asad Aali (ORCID: https://orcid.org/0009-0008-2120-5722)
- Jonathan I. Tamir (ORCID: https://orcid.org/0000-0001-9113-9566)
- Yamin Arefeen (ORCID: https://orcid.org/0000-0003-2194-1945)
- Sidharth Kumar
Institutions
- Intel (United States) (US)
- The University of Texas MD Anderson Cancer Center (US)
- The University of Texas at Austin (US)
- Stanford University (US)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1002/mrm.30591
- Citations
- 3
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- National Science Foundation
- Oracle
- National Institutes of Health