Flepp Roman, Nissen Leon, Sigrist Bastian, Nieuwland Arend, Cavalcanti Nicola, Fürnstahl Philipp, Dreher Thomas, Calvet Lilian
University Children's Hospital Zürich, Zurich, Switzerland.
Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zurich, Zurich, Switzerland.
Int J Comput Assist Radiol Surg. 2025 May 20. doi: 10.1007/s11548-025-03391-4.
Accurate intraoperative X-ray/CT registration is essential for surgical navigation in orthopedic procedures. However, existing methods struggle with consistently achieving sub-millimeter accuracy, robustness under broad initial pose estimates or need manual key-point annotations. This work aims to address these challenges by proposing a novel multi-view X-ray/CT registration method for intraoperative bone registration.
The proposed registration method consists of a multi-view, contour-based iterative closest point (ICP) optimization. Unlike previous methods, which attempt to match bone contours across the entire silhouette in both imaging modalities, we focus on matching specific subcategories of contours corresponding to bone substructures. This leads to reduced ambiguity in the ICP matches, resulting in a more robust and accurate registration solution. This approach requires only two X-ray images and operates fully automatically. Additionally, we contribute a dataset of 5 cadaveric specimens, including real X-ray images, X-ray image poses and the corresponding CT scans.
The proposed registration method is evaluated on real X-ray images using mean reprojection error (mRPD). The method consistently achieves sub-millimeter accuracy with a mRPD 0.67 mm compared to 5.35 mm by a commercial solution requiring manual intervention. Furthermore, the method offers improved practical applicability, being fully automatic.
Our method offers a practical, accurate, and efficient solution for multi-view X-ray/CT registration in orthopedic surgeries, which can be easily combined with tracking systems. By improving registration accuracy and minimizing manual intervention, it enhances intraoperative navigation, contributing to more accurate and effective surgical outcomes in computer-assisted surgery (CAS). The source code and the dataset are publicly available at: https://github.com/rflepp/MultiviewXrayCT-Registration .
准确的术中X射线/CT配准对于骨科手术中的手术导航至关重要。然而，现有方法在始终实现亚毫米级精度、在广泛的初始姿态估计下的鲁棒性方面存在困难，或者需要手动关键点标注。这项工作旨在通过提出一种用于术中骨配准的新型多视图X射线/CT配准方法来应对这些挑战。
所提出的配准方法由基于轮廓的多视图迭代最近点（ICP）优化组成。与之前试图在两种成像模态的整个轮廓上匹配骨轮廓的方法不同，我们专注于匹配与骨子结构相对应的特定轮廓子类别。这减少了ICP匹配中的模糊性，从而产生更鲁棒和准确的配准解决方案。这种方法只需要两张X射线图像，并且完全自动运行。此外，我们提供了一个包含5个尸体标本的数据集，包括真实的X射线图像、X射线图像姿态和相应的CT扫描。
使用平均重投影误差（mRPD）在真实X射线图像上评估所提出的配准方法。该方法始终实现亚毫米级精度，mRPD为0.67毫米，而需要人工干预的商业解决方案的mRPD为5.35毫米。此外，该方法具有更好的实际适用性，是完全自动的。
我们的方法为骨科手术中的多视图X射线/CT配准提供了一种实用、准确且高效的解决方案，该方案可以很容易地与跟踪系统相结合。通过提高配准精度并最小化人工干预，它增强了术中导航，有助于在计算机辅助手术（CAS）中实现更准确有效的手术结果。源代码和数据集可在以下网址公开获取：https://github.com/rflepp/MultiviewXrayCT-Registration 。
