Application of deep learning reconstruction combined with time-resolved post-processing method to improve image quality in CTA derived from low-dose cerebral CT perfusion data
Combining DLR with tMIP or tAve allows for reduction in radiation dose by about 33% in single peak arterial phase and 18% in total in CTP scanning, while further improving image quality of CTA derived from CTP data when compared to HIR.
Authors
- Min Xu (ORCID: https://orcid.org/0000-0002-8804-0431)
- Xiaobo Zhang (ORCID: https://orcid.org/0000-0002-6598-0519)
- Zhengyu Jin
- Jian Wang (ORCID: https://orcid.org/0000-0003-2291-640X)
- Yù Chen (ORCID: https://orcid.org/0000-0001-9795-9311)
- Tong Su
- Haozhe Liu (ORCID: https://orcid.org/0000-0003-1191-3529)
- Ming Yao (ORCID: https://orcid.org/0009-0009-9973-148X)
- Jing Tong (ORCID: https://orcid.org/0000-0002-5649-3524)
- Yanling Wang (ORCID: https://orcid.org/0000-0002-8589-4239)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Beijing Chaoyang Emergency Medical Center (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1186/s12880-025-01623-2
- Citations
- 2
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Natural Science Foundation of China
- Peking Union Medical College Hospital