Deep learning-enhanced super-resolution diffusion-weighted liver MRI: improved image quality, diagnostic performance, and acceleration
Diffusion-weighted liver MRI commonly suffers from limited image quality and efficiency. Deep learning reconstruction substantially improves liver MRI quality while enabling significantly shorter acquisition times. Improved lesion differentiation enables more accurate clinical diagnosis of liver lesions.
Authors
- Dan Zhao (ORCID: https://orcid.org/0000-0001-8854-4149)
- Feng Pan (ORCID: https://orcid.org/0000-0003-2820-768X)
- Jiayu Wan (ORCID: https://orcid.org/0000-0003-4603-3265)
- Xiangchuang Kong (ORCID: https://orcid.org/0000-0001-5740-0425)
- Ziyi Liu (ORCID: https://orcid.org/0000-0002-2460-6782)
- Kun Yang (ORCID: https://orcid.org/0000-0002-6782-6689)
- Peng Sun (ORCID: https://orcid.org/0000-0003-3868-4180)
- Lian Yang (ORCID: https://orcid.org/0000-0003-4664-2474)
- Chuansheng Zheng
Institutions
- Union Hospital (HK)
- Philips (Finland) (FI)
- Hubei Provincial Center for Disease Control and Prevention (CN)
- Wuhan Union Hospital (CN)
- Union Hospital (CN)
- Huazhong University of Science and Technology (CN)
- Hubei University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1186/s13244-025-02150-y
- Primary Topic
- MRI in cancer diagnosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China