Quantitative susceptibility mapping via deep neural networks with iterative reverse concatenations and recurrent modules
Overall, the proposed IR<sup>2</sup>QSM showed superior QSM results compared to iterative and deep learning-based methods, offering a more accurate QSM solution for clinical applications.
Authors
- Hongfu Sun (ORCID: https://orcid.org/0000-0003-3436-7831)
- Yin Liu (ORCID: https://orcid.org/0000-0003-3388-2575)
- Shanshan Shan (ORCID: https://orcid.org/0000-0002-8758-420X)
- Pengfei Rong (ORCID: https://orcid.org/0000-0001-5473-1982)
- Chen Chen (ORCID: https://orcid.org/0000-0003-3957-7061)
- Feng Liu (ORCID: https://orcid.org/0000-0002-1074-2601)
- Min Li (ORCID: https://orcid.org/0000-0002-0188-1394)
- Zhuang Xiong (ORCID: https://orcid.org/0009-0003-8849-5756)
- Yang Gao (ORCID: https://orcid.org/0000-0002-6841-9276)
Institutions
- Central South University (CN)
- The University of Queensland (AU)
- Soochow University (CN)
- Third Xiangya Hospital (CN)
- University of Newcastle Australia (AU)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-03-16
- DOI
- https://doi.org/10.1002/mp.17747
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- National Natural Science Foundation of China