A two-step automatic identification of contrast phases for abdominal CT images based on residual networks
Accurate identification of contrast phases is crucial in abdominal CT imaging. The two-step ResNet-based model achieved superior accuracy across internal and external datasets. Automated phase classification strengthens imaging quality control and supports precision AI applications.
Authors
- Jiawen Luo
- Zhenghan Yang (ORCID: https://orcid.org/0000-0003-3986-1732)
- Yan Zhang (ORCID: https://orcid.org/0000-0001-9710-6095)
- Jiang Jia-hui
- Chenguang Liu (ORCID: https://orcid.org/0009-0002-9121-5623)
- Qinghua Liu (ORCID: https://orcid.org/0000-0003-4090-3311)
- Kewei Wu
- Yuyang Yin (ORCID: https://orcid.org/0009-0005-7443-6194)
- Nan Sun (ORCID: https://orcid.org/0009-0007-0300-4268)
- Te Ba
- Airong Lv (ORCID: https://orcid.org/0009-0006-8032-8234)
- Hui Xu
Institutions
- Capital Medical University (CN)
- Dalian Medical University (CN)
- Peking University (CN)
- Mianyang Third People's Hospital (CN)
- Peking University Cancer Hospital (CN)
- Second Affiliated Hospital of Dalian Medical University (CN)
- Sichuan Mianyang 404 Hospital (CN)
- Beijing Hospital (CN)
- Peking University First Hospital (CN)
- United Imaging Healthcare (China) (CN)
- Peking University Third Hospital (CN)
- Beijing Friendship Hospital (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1186/s13244-025-01995-7
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support
- National Key Research and Development Program of China