Point cloud registration algorithm using liver vascular skeleton feature with computed tomography and ultrasonography image fusion.

Purpose: Radiofrequency ablation for liver cancer has advanced rapidly. For accurate ultrasound-guided soft-tissue puncture surgery, it is necessary to fuse intraoperative ultrasound images with preoperative computed tomography images. However, the conventional method is difficult to estimate and fuse images accurately. To address this issue, the present study proposes an algorithm for registering cross-source point clouds based on not surface but the geometric features of the vascular point cloud.

Methods: We developed a fusion system that performs cross-source point cloud registration between ultrasound and computed tomography images, extracting the node, skeleton, and geomatic feature of the vascular point cloud. The system completes the fusion process in an average of 14.5 s after acquiring the vascular point clouds via ultrasound.

Results: The experiments were conducted to fuse liver images by the dummy model and the healthy participants, respectively. The results show the proposed method achieved a registration error within 1.4 mm and decreased the target registration error significantly compared to other methods in a liver dummy model registration experiment. Furthermore, the proposed method achieved the averaged RMSE within 2.23 mm in a human liver vascular skeleton.

Conclusion: The study concluded that because the registration method using vascular feature point cloud could realize the rapid and accurate fusion between ultrasound and computed tomography images, the method is useful to apply the real puncture surgery for radiofrequency ablation for liver. In future work, we will evaluate the proposed method by the patients.