Investigating the Use of Generative Adversarial Networks-Based Deep Learning for Reducing Motion Artifacts in Cardiac Magnetic Resonance
GAN-based deep learning technology effectively reduces motion artifacts present in CMR cine images, demonstrating significant potential for clinical application in optimizing CMR motion artifact management.
Authors
- Tianle Zhang (ORCID: https://orcid.org/0000-0003-4881-2406)
- Ze-Peng Ma
- Xiaodan Zhang (ORCID: https://orcid.org/0000-0001-8621-1113)
- Wei Zheng
- Gaoyang Li (ORCID: https://orcid.org/0000-0003-0820-8108)
- Yongxia Zhao
- Yueming Zhu (ORCID: https://orcid.org/0000-0002-3101-6238)
- Shuang-Rui Yuan
Institutions
- Affiliated Hospital of Hebei University (CN)
- Hebei University (CN)
Publication Details
- Journal
- Journal of Multidisciplinary Healthcare
- Published
- 2025-02-01
- DOI
- https://doi.org/10.2147/jmdh.s492163
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Health Commission of Hebei Province
- Hebei University