Ultra-low-dose coronary CT angiography via super-resolution deep learning reconstruction: impact on image quality, coronary plaque, and stenosis analysis
Question How can radiation dose for coronary CT angiography be reduced without compromising image quality or affecting clinical decisions? Finding Super-resolution deep learning reconstruction (SR-DLR) algorithm allows for 60% dose reduction while ensuring high image quality and excellent performance in coronary plaque and stenosis analysis. Clinical relevance Dose optimization via SR-DLR has no detrimental effect on image quality, coronary plaque quantification and characterization, and stenosis severity analysis, which paves the way for its implementation in clinical practice.
Authors
- Min Xu (ORCID: https://orcid.org/0000-0002-8804-0431)
- Cheng Xu (ORCID: https://orcid.org/0000-0003-4056-8082)
- Lan Zou (ORCID: https://orcid.org/0000-0003-2443-0817)
- Yining Wang (ORCID: https://orcid.org/0000-0003-3193-2487)
- Ming Wang (ORCID: https://orcid.org/0000-0003-1071-4472)
- Yun Wang (ORCID: https://orcid.org/0009-0008-9502-764X)
- Zicheng Zhao
- Ke Xu (ORCID: https://orcid.org/0000-0003-2620-4960)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
Publication Details
- Journal
- European Radiology
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1007/s00330-025-11399-2
- Citations
- 11
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 14.96
Funders
- Peking Union Medical College Hospital
- National Science Fund for Distinguished Young Scholars