Kojcev Risto, Fuerst Bernhard, Zettinig Oliver, Fotouhi Javad, Lee Sing Chun, Frisch Benjamin, Taylor Russell, Sinibaldi Edoardo, Navab Nassir
Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA.
Center for Micro-BioRobotics, Istituto Italiano di Tecnologia, Pontedera, PI, Italy.
Int J Comput Assist Radiol Surg. 2016 Jun;11(6):1173-81. doi: 10.1007/s11548-016-1408-1. Epub 2016 Apr 20.
Precise needle placement is an important task during several medical procedures. Ultrasound imaging is often used to guide the needle toward the target region in soft tissue. This task remains challenging due to the user's dependence on image quality, limited field of view, moving target, and moving needle. In this paper, we present a novel dual-robot framework for robotic needle insertions under robotic ultrasound guidance.
We integrated force-controlled ultrasound image acquisition, registration of preoperative and intraoperative images, vision-based robot control, and target localization, in combination with a novel needle tracking algorithm. The framework allows robotic needle insertion to target a preoperatively defined region of interest while enabling real-time visualization and adaptive trajectory planning to provide safe and quick interactions. We assessed the framework by considering both static and moving targets embedded in water and tissue-mimicking gelatin.
The presented dual-robot tracking algorithms allow for accurate needle placement, namely to target the region of interest with an error around 1 mm.
To the best of our knowledge, we show the first use of two independent robots, one for imaging, the other for needle insertion, that are simultaneously controlled using image processing algorithms. Experimental results show the feasibility and demonstrate the accuracy and robustness of the process.
在多种医疗程序中,精确的针放置是一项重要任务。超声成像常被用于引导针朝向软组织中的目标区域。由于用户对图像质量、有限视野、移动目标和移动针的依赖,该任务仍然具有挑战性。在本文中,我们提出了一种用于在机器人超声引导下进行机器人针插入的新型双机器人框架。
我们将力控超声图像采集、术前和术中图像配准、基于视觉的机器人控制以及目标定位相结合,并结合一种新型针跟踪算法。该框架允许机器人针插入术前定义的感兴趣区域,同时实现实时可视化和自适应轨迹规划,以提供安全快速的交互。我们通过考虑嵌入水和仿组织明胶中的静态和移动目标来评估该框架。
所提出的双机器人跟踪算法允许精确的针放置,即以约1毫米的误差靶向感兴趣区域。
据我们所知,我们首次展示了使用两个独立的机器人,一个用于成像,另一个用于针插入,它们使用图像处理算法同时进行控制。实验结果表明了该方法的可行性,并证明了该过程的准确性和鲁棒性。