Multimodal Point Cloud Registration Method Based on Centerline-Guided Expansion and Contraction: An Optimization Strategy Applied in Bronchial Lumen Map Building

Abstract

In this work, a multimodal point cloud registration method using CT and video frames is proposed to optimize the modeling of the bronchial cavity environment. Preoperative CT data improve the quality of point clouds acquired from intraoperative video frames. Initially, preoperative CT scans are used to obtain bronchial point clouds and airway centerlines, while intraoperative bronchial point clouds and endoscope trajectories are captured in real-time using SLAM. Given that intraoperative frame-by-frame mapping cannot be directly globally registered, multi-modal point clouds undergo local segmentation. Subsequently, the preoperative bronchial airway centerlines guide iterative scaling and adjustment of the preoperative CT point clouds, achieving precise registration between the CT and the video frame point clouds. Experimental results demonstrate a rapid and accurate enhancement in the quality of the intraoperative bronchial point cloud, providing more precise maps of the cavity environment for surgical robots. The method is validated and evaluated using CT and video frame data collected from ex vivo pig lungs, achieving intraoperative mapping accuracy of 0.5 millimeter, respectively. These results surpass those of methods relying solely on SLAM for intraoperative mapping.