Laboratory of Biomechanical Engineering, Faculty of Engineering Technology, MIRA Institute, University of Twente, Enschede, the Netherlands.
Orthopaedic Research Lab, Radboud University Medical Center, Nijmegen, the Netherlands.
PLoS One. 2018 Jun 13;13(6):e0199136. doi: 10.1371/journal.pone.0199136. eCollection 2018.
A fast and accurate intraoperative registration method is important for Computer-Aided Orthopedic Surgery (CAOS). A-mode ultrasound (US) is able to acquire bone surface data in a non-invasive manner. To utilize A-mode US in CAOS, a suitable registration algorithm is necessary with a small number of registration points and the presence of measurement errors. Therefore, we investigated the effects of (1) the number of registration points and (2) the Ultrasound Point Localization Error (UPLE) on the overall registration accuracy.
We proposed a new registration method (ICP-PS), including the Iterative Closest Points (ICP) algorithm and a Perturbation Search algorithm. This method enables to avoid getting stuck in the local minimum of ICP iterations and to find the adjacent global minimum. This registration method was subsequently validated in a numerical simulation and a cadaveric experiment using a 3D-tracked A-mode US system.
The results showed that ICP-PS outperformed the standard ICP algorithm. The registration accuracy improved with the addition of ultrasound registration points. In the numerical simulation, for 25 sample points with zero UPLE, the averaged registration error of ICP-PS reached 0.25 mm, while 1.71 mm for ICP, decreasing by 85.38%. In the cadaver experiment, using 25 registration points, ICP-PS achieved an RMSE of 2.81 mm relative to 5.84 mm for the ICP, decreasing by 51.88%.
The simulation approach provided a well-defined framework for estimating the necessary number of ultrasound registration points and acceptable level of UPLE for a given required level of accuracy for intraoperative registration in CAOS. ICP-PS method is suitable for A-mode US based intraoperative registration. This study would facilitate the application of A-mode US probe in registering the point cloud to a known shape model, which also has the potential for accurately estimating bone position and orientation for skeletal motion tracking and surgical navigation.
计算机辅助骨科手术（CAOS）需要一种快速准确的术中配准方法。A 型超声（US）能够以非侵入性的方式获取骨表面数据。为了在 CAOS 中使用 A 型 US，需要一种合适的配准算法，该算法需要较少的配准点，并具有测量误差。因此，我们研究了（1）配准点数量和（2）超声点定位误差（UPLE）对整体配准精度的影响。
我们提出了一种新的配准方法（ICP-PS），包括迭代最近点（ICP）算法和摄动搜索算法。该方法可以避免 ICP 迭代的局部最小值，并找到相邻的全局最小值。该配准方法随后在使用 3D 跟踪 A 型 US 系统的数值模拟和尸体实验中得到了验证。
结果表明，ICP-PS 优于标准 ICP 算法。随着超声配准点的增加，配准精度得到提高。在数值模拟中，对于 25 个零 UPLE 的样本点，ICP-PS 的平均配准误差达到 0.25mm，而 ICP 为 1.71mm，降低了 85.38%。在尸体实验中，使用 25 个配准点，ICP-PS 与 ICP 相比，相对误差达到 2.81mm，降低了 51.88%。
模拟方法为估计术中配准所需的超声配准点数量和可接受的 UPLE 水平提供了一个明确的框架，以达到给定的术中配准精度要求。ICP-PS 方法适用于基于 A 型 US 的术中配准。本研究将有助于将 A 型 US 探头应用于将点云与已知形状模型进行配准，这也有可能准确估计骨骼运动跟踪和手术导航中的骨骼位置和方向。
