Magnetic resonance image denoising for Rician noise using a novel hybrid transformer‐CNN network (HTC‐net) and self‐supervised pretraining
The proposed method can effectively enhance the quality of MR images, which helps improve the diagnosis accuracy in clinical.
Authors
- Fumin Guo (ORCID: https://orcid.org/0000-0001-6220-1455)
- Lei Shi (ORCID: https://orcid.org/0000-0001-9100-897X)
- Sa Xiao (ORCID: https://orcid.org/0000-0002-4574-1406)
- Shengjie Shi
- Xin Hua Zhou (ORCID: https://orcid.org/0000-0002-5580-7907)
- Haidong Li (ORCID: https://orcid.org/0000-0001-9939-7164)
- Cheng Wang (ORCID: https://orcid.org/0000-0003-3796-4260)
- Xiuchao Zhao
Institutions
- Yangtze University (CN)
- Chinese Academy of Sciences (CN)
- Hainan University (CN)
- Wuhan Institute of Physics and Mathematics (CN)
- Wuhan National Laboratory for Optoelectronics (CN)
- University of Chinese Academy of Sciences (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1002/mp.17562
- Citations
- 8
- Primary Topic
- Image and Signal Denoising Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.99
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Natural Science Foundation of Hubei Province
- National Key Research and Development Program of China