In computer-assisted orthopedic surgery (CAOS), accurate pre-operative to intra-operative bone registration is an essential and critical requirement for providing navigational guidance. This registration process is challenging since the intra-operative 3D points are sparse, only partially overlapped with the pre-operative model, and disturbed by noise and outliers. The commonly used method in current state-of-the-art orthopedic robotic system is bony landmarks based registration, but it is very time-consuming for the surgeons. To address these issues, we propose a novel partial-to-full registration framework based on gradient-SDF for CAOS. The simulation experiments using bone models from publicly available datasets and the phantom experiments performed under both optical tracking and electromagnetic tracking systems demonstrate that the proposed method can provide more accurate results than standard benchmarks and be robust to 90% outliers. Importantly, our method achieves convergence in less than 1 second in real scenarios and mean target registration error values as low as 2.198 mm for the entire bone model. Finally, it only requires random acquisition of points for registration by moving a surgical probe over the bone surface without correspondence with any specific bony landmarks, thus showing significant potential clinical value.

翻译：在计算机辅助骨科手术（CAOS）中，精确的术前-术中骨组织配准是实现导航引导的关键前提。该配准过程面临诸多挑战：术中三维点云数据稀疏、仅与术前模型部分重叠，且易受噪声和异常值干扰。当前主流骨科机器人系统通常采用基于骨性标志点的配准方法，但该方法耗时较长。为解决上述问题，我们提出了一种基于梯度符号距离场（gradient-SDF）的新型局部-全局配准框架。通过公开数据集骨模型仿真实验，以及在光学追踪与电磁追踪系统下的体模实验表明：相较于基准方法，本方法能提供更高精度的配准结果，并对90%的异常值具有鲁棒性。值得注意的是，本方法在实际场景中收敛时间小于1秒，全骨模型平均靶向配准误差低至2.198毫米。该方法仅需通过手术探针在骨表面随机采集配准点云，无需依赖特定骨性标志点对应关系，展现出显著的临床应用潜力。