Synthesizing cone-beam projections in radiotherapy using deep learning network for patients with head and neck cancer
Yuhan Fan, Peng Huang, Jiawen Shang, Zhixing Chang, Zhihui Hu, Ke Zhang, Xin Xie, Zhiqiang Liu, Hui Yan · 2026
Read Synthesizing cone-beam projections in radiotherapy using deep learning network for patients with head and neck cancer by Yuhan Fan, Peng Huang, Jiawen Shang, Zhixing Chang, Zhihui Hu, Ke Zhang, Xin Xie, Zhiqiang Liu, Hui Yan (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13350679.txt.
Description
Yuhan Fan and co-authors used deep learning in 2026 to regenerate radiotherapy positioning images that clinics normally throw away. Cone-beam projections for head and neck cancer patients are bulky, so they are discarded after building the three-dimensional scan, yet later checks may need them.
Working from 50 patients, they first computed synthetic X-ray views from the stored scans, then trained networks to convert those into realistic projections. Three network designs were compared, with tests on scatter correction, scan sharpness, and training size. Regenerating discarded images from kept data could rescue old treatment records for review and research.
Catalog record
| CREATOR | Yuhan Fan, Peng Huang, Jiawen Shang, Zhixing Chang, Zhihui Hu, Ke Zhang, Xin Xie, Zhiqiang Liu, Hui Yan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30756 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |