A registration method for total hip arthroplasty navigation system based on point cloud alignment

Hip replacement surgery requires high precision in aligning intraoperative anatomical structure localization with preoperative imaging spatial alignment. To address the registration difficulties caused by the limited number and sparse distribution of intraoperative probe acquisition points, our paper proposes a method based on point cloud registration between an intraoperative probe tracked by a passive binocular optical tracking system (NDI) and a CT surface, aimed at improving patient registration accuracy in intraoperative navigation scenarios. First, we define the patient-registration problem and describe the methods for acquiring pre-operative and intra-operative data. Subsequently, the centroids of the NDI optical tracking markers are employed as common features shared by the patient and image spaces, and an initial registration is performed using a three-point method. Finally, an improved ICP algorithm is used to perform accurate registration between the two point clouds, and the registration results are validated using a fixture platform. In the experiments conducted on 3D-printed acetabular and femoral models, the target registration errors (TRE) of the proposed method for registering the acetabular model and the femoral model are (0.55 $\pm$ 0.15) mm and (0.67 $\pm$ 0.18) mm, respectively. The experimental results demonstrate that the proposed method achieves good registration accuracy and stability under phantom experimental conditions. Using the TRE as the primary evaluation metric to measure the spatial localization error of target points, the method exhibits superior geometric registration performance compared to the traditional iterative closest point (ICP) algorithm. Additionally, it features a concise workflow and ease of implementation, providing a feasible technical solution for sparse point cloud registration in intraoperative navigation scenarios.