从一张普通X光片重建骨骼表面，精度提升近60%。
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation
- 通过双面深度估计同时学习骨骼前后表面的三维结构。
- 表面重建误差从4.78毫米降至1.96毫米，显著提升精度。
- 适合骨科临床应用，兼顾高精度与计算效率。
X光检查因其成本低、辐射少，在骨科中广泛应用。从单张二维X光片实现三维重建（即2D-3D重建）具有广阔临床潜力，但实现临床可用的精度与计算效率仍是未解难题。与计算机视觉其他领域不同，X光成像特有的射线穿透性和固定几何特性尚未被充分挖掘。本文提出一种新方法，通过同时学习来自X光图像的多个深度图（多骨前后表面），实现与计算机断层扫描（CT）的配准。该方法充分利用了X光成像的固定几何特性，显著提升了整体表面重建精度。研究使用600例CT和2651张X光片（每名患者4至5张不同体位的X光片），结果表明，本方法相比传统方法将表面重建误差由4.78毫米降低至1.96毫米。这一显著精度提升及更高的计算效率，预示其在临床中的应用潜力。
原文摘要 · Abstract (English)
Radiography is widely used in orthopedics for its affordability and low radiation exposure. 3D reconstruction from a single radiograph, so-called 2D-3D reconstruction, offers the possibility of various clinical applications, but achieving clinically viable accuracy and computational efficiency is still an unsolved challenge. Unlike other areas in computer vision, X-ray imaging's unique properties, such as ray penetration and fixed geometry, have not been fully exploited. We propose a novel approach that simultaneously learns multiple depth maps (front- and back-surface of multiple bones) derived from the X-ray image to computed tomography registration. The proposed method not only leverages the fixed geometry characteristic of X-ray imaging but also enhances the precision of the reconstruction of the whole surface. Our study involved 600 CT and 2651 X-ray images (4 to 5 posed X-ray images per patient), demonstrating our method's superiority over traditional approaches with a surface reconstruction error reduction from 4.78 mm to 1.96 mm. This significant accuracy improvement and enhanced computational efficiency suggest our approach's potential for clinical application.
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