Real-Time Reconstruction of 3D Bone Models via Very-Low-Dose Protocols
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 56 references that are in the map. First posted on arXiv 19 Aug 2025.
Builds on
7 of 56 references.
References · 7
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
- 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
- 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
- 2020End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Imagescited by 28
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Citing papers per year: 2026: 1.
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- 2023BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images
- 2026Displacement Preserving Relational Distillation for Robust Medical Segmentation
- 2026JDCNet: Confidence-Gated Privileged-Modality Distillation for Cost-Preserving X-ray Inference
- 2026GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization
- 20262D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models