Accelerated proton resonance frequency‐based magnetic resonance thermometry by optimized deep learning method
This study demonstrates that deep learning-based reconstruction significantly enhances the accuracy and efficiency of MR thermometry, particularly in the context of FUS-based clinical treatments for uterine fibroids. This approach could also be extended to other applications such as essential tremor and prostate cancer treatments where MRI-guided FUS plays a critical role.
Authors
- Guofeng Shen (ORCID: https://orcid.org/0000-0002-7731-5399)
- Shenyan Zong (ORCID: https://orcid.org/0000-0002-4263-4947)
- Sijie Xu (ORCID: https://orcid.org/0000-0002-3663-2857)
- Chang‐Sheng Mei (ORCID: https://orcid.org/0000-0001-6600-0191)
- Yueran Zhao (ORCID: https://orcid.org/0009-0008-2067-7840)
- He Wang (ORCID: https://orcid.org/0000-0002-2053-9439)
Institutions
- Tongji University (CN)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Shanghai Jiao Tong University (CN)
- Soochow University (TW)
- Fudan University (CN)
- Shanghai Institute for Science of Science (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-05-31
- DOI
- https://doi.org/10.1002/mp.17909
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China