Real-Time Reconstruction of 3D Bone Models via Very-Low-Dose Protocols
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arXiv
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| Format: | Preprint |
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2025
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| author | Lin, Yiqun Sun, Haoran Li, Yongqing Aslam, Rabia Tse, Lung Fung Cheng, Tiange Chui, Chun Sing Yau, Wing Fung Meur, Victorine R. Le Amangeldy, Meruyert Cho, Kiho Ye, Yinyu Zou, James Zhao, Wei Li, Xiaomeng |
| author_facet | Lin, Yiqun Sun, Haoran Li, Yongqing Aslam, Rabia Tse, Lung Fung Cheng, Tiange Chui, Chun Sing Yau, Wing Fung Meur, Victorine R. Le Amangeldy, Meruyert Cho, Kiho Ye, Yinyu Zou, James Zhao, Wei Li, Xiaomeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13947 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time Reconstruction of 3D Bone Models via Very-Low-Dose Protocols Lin, Yiqun Sun, Haoran Li, Yongqing Aslam, Rabia Tse, Lung Fung Cheng, Tiange Chui, Chun Sing Yau, Wing Fung Meur, Victorine R. Le Amangeldy, Meruyert Cho, Kiho Ye, Yinyu Zou, James Zhao, Wei Li, Xiaomeng Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Real-Time Reconstruction of 3D Bone Models via Very-Low-Dose Protocols |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.13947 |