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Purpose <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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: <a href="https://github.com/rflepp/MultiviewXrayCT-Registration">https://github.com/rflepp/MultiviewXrayCT-Registration</a>.</p>

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Automatic multi-view X-ray/CT registration using bone substructure contours

  • 期刊论文

  • 发表日期：

    2025年5月20日

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  • Roman Flepp,
  • Leon Nissen,
  • Bastian Sigrist,
  • Arend Nieuwland,
  • Nicola Cavalcanti,
  • Philipp Fürnstahl,
  • Thomas Dreher,
  • Lilian Calvet

摘要

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.