Thoracic Cartilage Ultrasound-CT Registration Using Dense Skeleton Graph

Autonomous ultrasound (US) imaging has gained increased interest recently, and it has been seen as a potential solution to overcome the limitations of free-hand US exami-nations, such as inter-operator variations. However, it is still challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications with high acoustic-impedance bone structures below the skin. To address this challenge, a dense graph-based non-rigid registration is proposed to transfer planned paths from the atlas to the current setup by explicitly considering subcutaneous bone surface. To this end, the sternum and cartilage branches are segmented using a template matching to assist coarse alignment of US and CT point clouds. Afterward, a directed graph is generated based on the CT template. Then, the self-organizing map using geographical distance is successively performed twice to extract the optimal graph representations for CT and US point clouds, individually. To evaluate the proposed approach, five cartilage point clouds from distinct patients are employed. The results demonstrate that the proposed graph-based registration can effectively map trajectories from CT to the current setup to do US examination through limited intercostal space. The non-rigid registration results in terms of Hausdorff distance (Mean±SD) is $9.48 \pm 0.27$ mm and the path transferring error in terms of Euclidean distance is $2.21\pm 1.11\ mm$. The code¹¹https://github.com/marslicy/Cartilage-graph-based-US-CT-Registration and video²²Video: https://www.youtube.com/watch?v=QJz2fkwgbP8 can be publicly accessed.

I. Introduction

Medical ultrasound (US) imaging has been widely used in the current clinical practices due to its radiation-free, non-invasive, and real-time characteristics. Furthermore, the low cost and availability of US make it become the primary tool for routine screening programs for internal lesions, particu-larly in the preliminary healthcare industry. However, free-hand US examination is highly user-dependent. To alleviate inter- and intra-variations among operators, the robotic US system (RUSS) has attracted increasing attention in recent years. Owing to the high accuracy, robotic techniques are employed to quantitatively control the imaging acquisition parameters [1], [2]. To have an in-depth view of RUSS, readers are referred to a survey article that summarizes the developments of RUSS from the perspective of technology complexity [3].