用一张全景片重建高精度3D牙科影像，辐射剂量更低
HICT: High-precision 3D CBCT reconstruction from a single X-ray
- 先用视频扩散模型生成多视角投影图
- 再通过动态注意力网络和采样策略重建高质量CBCT
- 自建500对数据集，适合临床精准诊断
精准的3D牙科成像对诊断与治疗规划至关重要，但锥形束CT（CBCT）的高辐射剂量和成本限制了其普及。从单张低剂量全景片重建3D体积是一种有前景的替代方案，但受限于几何不一致性和精度不足。本文提出HiCT，一种两阶段框架：第一阶段利用视频扩散模型从单张全景图像生成几何一致的多视角投影；第二阶段通过基于射线的动态注意力网络和X-ray采样策略重建高保真度的CBCT。为支持该方法，我们构建了XCT数据集，包含公开的CBCT数据及500对配对的PX-CBCT样本。大量实验表明，HiCT达到当前最优性能，可实现临床可用的准确且几何一致的重建。
原文摘要 · Abstract (English)
Accurate 3D dental imaging is vital for diagnosis and treatment planning, yet CBCT's high radiation dose and cost limit its accessibility. Reconstructing 3D volumes from a single low-dose panoramic X-ray is a promising alternative but remains challenging due to geometric inconsistencies and limited accuracy. We propose HiCT, a two-stage framework that first generates geometrically consistent multi-view projections from a single panoramic image using a video diffusion model, and then reconstructs high-fidelity CBCT from the projections using a ray-based dynamic attention network and an X-ray sampling strategy. To support this, we built XCT, a large-scale dataset combining public CBCT data with 500 paired PX-CBCT cases. Extensive experiments show that HiCT achieves state-of-the-art performance, delivering accurate and geometrically consistent reconstructions for clinical use.
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