In surgical navigation system-assisted pedicle screw fixation, preoperative and intraoperative point clouds registration accuracy is crucial for positioning and navigation. When the patient's preoperative space is accurately registered to the actual surgical space, the surgical instrument can be guided to the patient's surgical site, and the planned surgical path can be accurately implemented during the operation. The preoperative point cloud is obtained by reconstructing the patients' preoperative CT, while a structural light scanner obtains the intraoperative point cloud during the operation. The acquisition methods of two-point clouds differ; hence, their densities and initial poses are quite different. Therefore, they are cross-source point clouds. Moreover, the scanned intraoperative point cloud is a small part of an entire spine since the exposed spine is very limited during an operation. Hence, the overlapping ratio of the preoperative and intraoperative point clouds is low. Existing registration algorithms are prone to fail or derive a low accuracy in preoperative and intraoperative point cloud registration. To solve these problems, this study proposes a preoperative and intraoperative point clouds registration algorithm with cross-source and a small overlapping ratio for pedicle screw fixation.

This study proposes a preoperative and intraoperative point clouds registration algorithm with cross-source and low overlapping ratio based on Farthest Point Sampling (FPS). The proposed algorithm includes coarse and fine registration. The coarse registration comprises three steps. Firstly, the voxel filter was used to down-sample the intraoperative point cloud to bring its density close to the preoperative point cloud. Secondly, the Fast Point Feature Histogram (FPFH) features of the intraoperative point cloud were extracted. The FPS was used to sample the preoperative point cloud, and then the preoperative point cloud was divided into several local regions by kd tree algorithm. These local regions formed the candidate set. Thirdly, the candidate set was traversed to calculate the FPFH features of each local region. The Sample Consensus Initial Alignment (SAC-IA) feature matching method was to realize feature matching and pose transformation estimation of intraoperative point cloud. The distance errors deduced by the SAC-IA method between the intraoperative point cloud and local point cloud were compared and the local region with the minimum distance error was selected as the optimal local region. The transformation of the optimal local region was the intraoperative and preoperative points clouds coarse registration's transformation. In fine registration, the Iterative Closest Point (ICP) algorithm was adopted to further align the intraoperative and the preoperative point cloud. It is performed based on the coarse registration result. The optimal local region is used as the target point cloud at this stage. Using the FPS and SAC-IC methods, an optimal local region sampled from the preoperative point cloud is obtained, enabling the point clouds can align under large original pose difference and low overlapping ratio conditions. In fine registration, the ICP algorithm was adopted to further align the intraoperative and the preoperative point clouds. The fine registration was performed based on the coarse registration results. The optimal local region was used as the target point cloud at this stage.

Based on the FPS method, the preoperative point cloud is divided into several local regions (Fig. 2). An optimal local neighborhood is derived to complete registration with the intraoperative point cloud. This study adopts nine pairs of preoperative and intraoperative point clouds for testing, with different overlapping ratios and initial poses (Table 1). The visualization results of the registration process using the proposed algorithm are shown in Fig. 3, including the initial pose of nine pairs of point clouds, the sampling results of FPS, and the coarse and fine registration results. To evaluate the performance of the proposed algorithm, two state-of-the-art registration algorithms, FPFH+ICP and SHOT+ICP, are adopted for comparison (Table 2). The proposed algorithm achieves the minimum coarse registration error, providing better alignment for ICP fine registration. A comparison of the final registration transformation matrix with the ground truth shows that the average rotation error is 0.406° and the translation error is 0.474 mm, which meets clinical requirements. Simultaneously, the registration time of the proposed algorithm is less than 2 min, which is adequate for an operation. In addition, the registration success rates of the three algorithms in the experiment are compared (Table 3). The successfully registered FPFH+ICP and SHOT+ICP algorithm samples are 6 out of 9. While for the proposed algorithm, it is 9 out of 9, demonstrating that the registration success rate increased from 66.67% to 100%.

This study proposes a point cloud registration algorithm with cross-source and a low overlapping ratio for pedicle screw fixation. Through the registration of preoperative and intraoperative point clouds of a lumbar vertebra with an overlapping ratio of less than 3%, the experimental results show that the proposed algorithm based on FPS can resolve the problems of density differences, large initial pose differences, and low overlapping ratios in the preoperative and intraoperative point clouds registration of pedicle screw fixation assisted by a surgical navigation system. High precision registration can be realized, improving the accuracy and safety of surgical navigation systems. The research only considers the rigid transformation of preoperative and intraoperative point clouds. Preoperative and intraoperative intervertebral motion will be considered in the future to make the proposed system algorithm more suitable for clinical practice.