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Peer-reviewedCC BY-NC-NDQuantitative imaging in medicine and surgery

A competition-proven deep learning-based dual-domain framework for low-dose CBCT reconstruction

  • Xuzhi Zhao1
  • Muliang Li1
  • Yi Du2
  1. 1School of Information Network Security , People’s Public Security University of China , Beijing , ;
  2. 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) , Department of Radiation Oncology , Peking University Cancer Hospital & Institute , Beijing , ;
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Cite

BibTeXRIS
BibTeX
@article{zhao2026competitionproven,
  title = {{A competition-proven deep learning-based dual-domain framework for low-dose CBCT reconstruction}},
  author = {Xuzhi Zhao and Muliang Li and Yi Du},
  journal = {Quantitative imaging in medicine and surgery},
  year = {2026},
  month = aug,
  url = {https://europepmc.org/article/PMC/PMC13654160},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - A competition-proven deep learning-based dual-domain framework for low-dose CBCT reconstruction
AU  - Xuzhi Zhao
AU  - Muliang Li
AU  - Yi Du
PY  - 2026
DA  - 2026/08/30
JO  - Quantitative imaging in medicine and surgery
UR  - https://europepmc.org/article/PMC/PMC13654160
SN  - 2223-4292
SN  - 2223-4306
ER  -