Dual‐scan self‐learning denoising for application in ultralow‐field <scp>MRI</scp>
Theoretical and experimental implementations show that the proposed self-learning model achieves improved performance on magnitude image denoising with synthetic and real-world data at ULF. Additionally, we test our method on calculated phase and quantification images, demonstrating its superior performance over several contrastive methods.
Authors
- Wei He (ORCID: https://orcid.org/0000-0002-8306-0572)
- Zheng Xu (ORCID: https://orcid.org/0000-0003-3968-414X)
- Yuxiang Zhang (ORCID: https://orcid.org/0000-0002-8148-8006)
- Jiamin Wu (ORCID: https://orcid.org/0000-0002-4942-9137)
Institutions
- Chongqing University (CN)
- Shenzhen Academy of Aerospace Technology (CN)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1002/mrm.30600
- Citations
- 1
- Primary Topic
- Image and Signal Denoising Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.28
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Chongqing
- Science, Technology and Innovation Commission of Shenzhen Municipality