Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment
A data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.
Authors
- Hao Zeng (ORCID: https://orcid.org/0000-0001-9148-1716)
- Yumo Zhao (ORCID: https://orcid.org/0000-0002-4875-9441)
- Fei Kang (ORCID: https://orcid.org/0000-0003-0267-7819)
- Duo Zhang (ORCID: https://orcid.org/0000-0002-7372-6799)
- Yue Li (ORCID: https://orcid.org/0000-0003-2212-1324)
- Chen Sun
- Lei Shi (ORCID: https://orcid.org/0000-0002-6728-770X)
- Yihuan Lu (ORCID: https://orcid.org/0000-0002-1855-5288)
- Hao Liu (ORCID: https://orcid.org/0009-0004-2137-0892)
- Jing Wang (ORCID: https://orcid.org/0000-0001-7431-8925)
Institutions
- United Imaging Healthcare (China) (CN)
- Xijing Hospital (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2024-08-13
- DOI
- https://doi.org/10.1007/s00259-024-06872-x
- Citations
- 13
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.26
Funders
- National Natural Science Foundation of China