Department of Mechanical Engineering, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
School of Mechanical Engineering, Chonnam National University, Gwangju, South Korea.
Int J Med Robot. 2021 Aug;17(4):e2272. doi: 10.1002/rcs.2272. Epub 2021 May 12.
Needle insertions have been used in several minimally invasive procedures for diagnostic and therapeutic purposes. Real-time position of the needle tip is an important information in needle steering systems.
This work introduces a robot-assisted ultrasound tracking (R-AUST) system integrated with a needle shape prediction method to provide 3D position of the needle tip. The tracking system is evaluated in phantom and ex vivo beef liver tissues.
An average error of 0.60 mm was found for needle insertion tests inside the phantom tissue. The R-AUST integrated with shape prediction in the beef liver tissue was able to track the needle tip with an average and maximum error of 0.37 and 0.67 mm, respectively. The average error reported in this work is within the mean allowable needle placement error (<2.7 mm) in targeted procedures.
Integration of R-AUST tracking method with needle shape prediction results in a reasonably accurate real-time tracking suitable for ultrasound-guided needle insertions.
在一些微创诊断和治疗程序中,已经使用了针插入技术。针尖的实时位置是针导向系统中的一个重要信息。
这项工作介绍了一种集成了针形状预测方法的机器人辅助超声跟踪(R-AUST)系统,以提供针尖的 3D 位置。该跟踪系统在体模和离体牛肝组织中进行了评估。
在体模组织内进行的针插入测试中,发现平均误差为 0.60 毫米。R-AUST 与牛肝组织中的形状预测相结合,能够以平均和最大误差 0.37 和 0.67 毫米分别跟踪针尖。本工作中报告的平均误差在靶向手术中允许的平均可接受的针放置误差(<2.7 毫米)范围内。
将 R-AUST 跟踪方法与针形状预测相结合,可实现适用于超声引导针插入的合理准确的实时跟踪。