使用骨亚结构轮廓的自动多视图x射线/CT配准。

IF 2.3 3区 医学 Q3 ENGINEERING, BIOMEDICAL
Roman Flepp, Leon Nissen, Bastian Sigrist, Arend Nieuwland, Nicola Cavalcanti, Philipp Fürnstahl, Thomas Dreher, Lilian Calvet
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引用次数: 0

摘要

目的:准确的术中x线/CT定位对骨科手术导航至关重要。然而,现有的方法难以始终达到亚毫米精度,在广泛的初始姿态估计下具有鲁棒性,或者需要手动标注关键点。这项工作旨在通过提出一种新的术中骨配准的多视点x线/CT配准方法来解决这些挑战。方法:提出的配准方法包括一种多视图、基于轮廓的迭代最近点优化方法。与之前的方法不同,这些方法试图在两种成像模式中匹配整个轮廓的骨轮廓,我们专注于匹配与骨子结构对应的特定子类别的轮廓。这可以减少ICP匹配中的歧义,从而产生更健壮和准确的注册解决方案。这种方法只需要两张x射线图像,并且完全自动操作。此外,我们还提供了5具尸体标本的数据集,包括真实的x射线图像,x射线图像姿势和相应的CT扫描。结果:采用平均重投影误差(mRPD)对所提出的配准方法进行了评价。该方法始终实现亚毫米精度,mRPD为0.67 mm,而需要人工干预的商业解决方案的mRPD为5.35 mm。此外,该方法的自动化程度高,实用性强。结论:该方法为骨科手术中多视点x线/CT配准提供了一种实用、准确、高效的解决方案,可方便地与跟踪系统结合使用。通过提高配准精度和减少人工干预,增强术中导航,有助于在计算机辅助手术(CAS)中获得更准确和有效的手术结果。源代码和数据集可在:https://github.com/rflepp/MultiviewXrayCT-Registration上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic multi-view X-ray/CT registration using bone substructure contours.

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 .

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来源期刊
International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery ENGINEERING, BIOMEDICAL-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
5.90
自引率
6.70%
发文量
243
审稿时长
6-12 weeks
期刊介绍: The International Journal for Computer Assisted Radiology and Surgery (IJCARS) is a peer-reviewed journal that provides a platform for closing the gap between medical and technical disciplines, and encourages interdisciplinary research and development activities in an international environment.
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