Real-time reconstruction of 3D bone models via very-low-dose protocols
Patient-specific bone models are essential for designing surgical guides and preoperative planning, as they enable the visualization of intricate anatomical structures. However, traditional CT-based approaches for creating bone models are limited to preoperative use due to the low flexibility and high radiation exposure of CT and time-consuming manual delineation. Here, we introduce Semi-Supervised Reconstruction with Knowledge Distillation (SSR-KD), a fast and accurate AI framework to reconstruct high-quality bone models from biplanar X-rays in 30 seconds, with an average error under 1.0 mm, eliminating the dependence on CT and manual work. Additionally, high tibial osteotomy simulation was performed by experts on reconstructed bone models, demonstrating that bone models reconstructed from biplanar X-rays have comparable clinical applicability to those annotated from CT. Overall, our approach accelerates the process, reduces radiation exposure, enables intraoperative guidance, and significantly improves the practicality of bone models, offering transformative applications in orthopedics.
Authors
- Yiqun Lin (ORCID: https://orcid.org/0000-0002-4726-5992)
- Haoran Sun (ORCID: https://orcid.org/0009-0007-7390-2733)
- KiHo Cho
- Yinyu Ye (ORCID: https://orcid.org/0009-0001-3239-2622)
- Lung Fung Tse
- Tiange Cheng
- Chun Sing Chui
- James Zou
- Victorine R. Le Meur
- Wing Fung Yau
- Wei Zhao
- Yongqing Li
- Meruyert Amangeldy
- Rabia Aslam
- Xiaomeng Li
Institutions
- Zhejiang International Studies University (CN)
- Union Hospital (HK)
- Chinese University of Hong Kong (HK)
- Hong Kong University of Science and Technology (HK)
- Hong Kong Science and Technology Parks Corporation (HK)
- Zhejiang Lab (CN)
- Beihang University (CN)
- University of Hong Kong (HK)
- Stanford University (US)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-03-17
- DOI
- https://doi.org/10.1038/s41746-026-02389-9
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Innovation and Technology Fund
- Natural Science Foundation of Zhejiang Province