Deep learning-based bone removal in head and neck computed tomography angiography: a comparative study with conventional algorithms
The CNN-based bone removal algorithm enhances vascular visualization in head and neck CTA, particularly at 100 kVp, offering superior segmentation accuracy even at lower radiation dose. These findings advocate for its integration into clinical workflows to improve cerebrovascular diagnostics.
Authors
- Linfeng Li (ORCID: https://orcid.org/0000-0002-1970-568X)
- Jie Liu (ORCID: https://orcid.org/0000-0003-0895-7598)
- Jie Gao (ORCID: https://orcid.org/0000-0002-6580-0581)
- Yicun Zhang
- Beibei Shao
Institutions
- Zhengzhou University of Aeronautics (CN)
- Siemens Healthcare (United States) (US)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-10-29
- DOI
- https://doi.org/10.21037/qims-2025-942
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00