Artificial Intelligence-Assisted Compressed Sensing Technique Accelerates Magnetic Resonance Imaging Simulation for Head and Neck Cancer Radiation Therapy
Compared to PI, ACS can significantly accelerate MRI simulation for head and neck cancer radiation therapy without compromising image quality and degrading the guidance role of tumor target delineation.
Authors
- Tiffany Dong (ORCID: https://orcid.org/0000-0002-0086-9289)
- Shengmei Zhou (ORCID: https://orcid.org/0000-0002-1917-5543)
- Muh-Shi Lin (ORCID: https://orcid.org/0000-0002-9798-8160)
- Haoqiang He (ORCID: https://orcid.org/0000-0002-5829-5492)
- Shaojin Wang (ORCID: https://orcid.org/0000-0003-4949-3910)
- Yu Luo
- Wenjun Fan (ORCID: https://orcid.org/0000-0002-3012-9951)
- Lintao Shang
- Feng Chi (ORCID: https://orcid.org/0000-0002-7111-6276)
Institutions
- Sun Yat-sen University (CN)
- Chinese PLA General Hospital (CN)
- Foshan Maternity and Child Health Care Hospital (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- Advances in Radiation Oncology
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1016/j.adro.2025.101819
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- Sun Yat-sen University
- Sun Yat-sen University Cancer Center
- Basic and Applied Basic Research Foundation of Guangdong Province