Purpose: Central venous catheterization (CVC) is a critical medical procedure for vascular access, hemodynamic monitoring, and life-saving interventions. Its success remains challenging due to the need for continuous ultrasound-guided visualization of a target vessel and approaching needle, which is further complicated by anatomical variability and operator dependency. Errors in needle placement can lead to life-threatening complications. While robotic systems offer a potential solution, achieving full autonomy remains challenging. In this work, we propose an end-to-end robotic-ultrasound-guided CVC pipeline, from scan initialization to needle insertion. Methods: We introduce a deep-learning model to identify clinically relevant anatomical landmarks from a depth image of the patient's neck, obtained using RGB-D camera, to autonomously define the scanning region and paths. Then, a robot motion planning framework is proposed to scan, segment, reconstruct, and localize vessels (veins and arteries), followed by the identification of the optimal insertion zone. Finally, a needle guidance module plans the insertion under ultrasound guidance with operator's feedback. This pipeline was validated on a high-fidelity commercial phantom across 10 simulated clinical scenarios. Results: The proposed pipeline achieved 10 out of 10 successful needle placements on the first attempt. Vessels were reconstructed with a mean error of 2.15 \textit{mm}, and autonomous needle insertion was performed with an error less than or close to 1 \textit{mm}. Conclusion: To our knowledge, this is the first robotic CVC system demonstrated on a high-fidelity phantom with integrated planning, scanning, and insertion. Experimental results show its potential for clinical translation.

翻译：目的：中心静脉置管（CVC）是用于血管通路、血流动力学监测和挽救生命干预的关键医疗程序。由于需要持续超声引导下对目标血管和进针路径进行可视化，其成功实施仍具挑战性，且解剖结构变异性和操作者依赖性进一步增加了难度。穿刺针放置错误可能导致危及生命的并发症。虽然机器人系统提供了潜在的解决方案，但实现完全自主仍面临挑战。在本工作中，我们提出了一种从扫描初始化到穿刺针插入的端到端机器人-超声引导CVC流程。方法：我们引入一种深度学习模型，从通过RGB-D相机获取的患者颈部深度图像中识别临床相关的解剖标志，以自主定义扫描区域和路径。随后，提出一种机器人运动规划框架，用于扫描、分割、重建和定位血管（静脉和动脉），继而确定最佳穿刺区域。最后，穿刺针引导模块在超声引导下结合操作者反馈规划穿刺过程。该流程在10个模拟临床场景的高保真商用体模上进行了验证。结果：所提流程在首次尝试中实现了10/10的成功穿刺针放置。血管重建平均误差为2.15 \textit{mm}，自主穿刺针插入误差小于或接近1 \textit{mm}。结论：据我们所知，这是首个在高保真体模上演示的、集成规划、扫描与穿刺功能的机器人CVC系统。实验结果表明其具有临床转化的潜力。