Deep Learning–Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction
Compared with the conventional compressed sensing reconstruction, the DL-based super-resolution reconstruction demonstrated superior image quality and was feasible for achieving higher diagnostic confidence in infarction stroke.
Authors
- Meimeng Zhong (ORCID: https://orcid.org/0009-0004-2594-059X)
- Liangjie Lin (ORCID: https://orcid.org/0000-0002-3174-3136)
- Haonan Zhang (ORCID: https://orcid.org/0009-0008-3231-0480)
- Qingwei Song (ORCID: https://orcid.org/0000-0002-7035-7211)
- Xulun Lu (ORCID: https://orcid.org/0009-0001-0858-6910)
- Yan Zhao (ORCID: https://orcid.org/0009-0003-5345-4794)
- Shuo Zhang (ORCID: https://orcid.org/0009-0001-5671-6273)
- Chao Yang (ORCID: https://orcid.org/0000-0002-7349-4721)
- Hongbo Feng (ORCID: https://orcid.org/0000-0002-8388-2621)
- Shuai Hu
- Nan Wang (ORCID: https://orcid.org/0000-0002-8842-3424)
- Hanxu Shenliu
Institutions
- Philips (Finland) (FI)
- Dalian Medical University (CN)
- Philips (China) (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
- China Medical University (CN)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2024-12-20
- DOI
- https://doi.org/10.3174/ajnr.a8482
- Citations
- 8
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 3.23