Robot Assisted Endovascular Intervention: Device Design and Innovation

机器人辅助血管内介入：设备设计与创新

• 批准号: EP/N024877/1
• 负责人: Guang-Zhong Yang
• 金额: $ 141.7万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN024877%2F1
• 关键词: Robot; Assisted; Endovascular; Intervention; Device

Vascular disease is the most common precursor to ischaemic heart disease and stroke, which are two of the leading causes of death worldwide. Advances in endovascular intervention in recent years have transformed patient survival rates and post-surgical quality of life. Compared to open surgery, it has the advantages of faster recovery, reduced need for general anaesthesia, reduced blood loss and significantly lower mortality. However, endovascular intervention involves complex manoeuvring of pre-shaped catheters to reach target areas in the vasculature. Some endovascular tasks can be challenging for even highly-skilled operators. The use of robot assisted endovascular intervention aims to address some of these difficulties, with the added benefit of allowing the operator to remotely control and manipulate devices, thus avoiding exposure to X-ray radiation. The purpose of this work is to develop a new robot-assisted endovascular platform, incorporating novel device designs with improved human-robot control. It builds on our strong partnership with industry aiming to develop the next generation robots that are safe, effective, and accessible to general NHS populations.

血管疾病是缺血性心脏病和中风的最常见先兆，这两种疾病是全球主要的死亡原因。近年来血管内介入治疗的进展改变了患者的存活率和术后生活质量。与开放手术相比，它具有恢复快、减少全身麻醉、减少出血量和明显降低死亡率的优点。然而，血管内介入包括复杂的操作预先成形的导管以到达血管系统的目标区域。一些血管内任务即使对于高技能的操作员来说也是具有挑战性的。机器人辅助血管内介入的使用旨在解决其中的一些困难，并增加了允许操作员远程控制和操纵设备的好处，从而避免暴露在X射线辐射中。这项工作的目的是开发一种新的机器人辅助血管内平台，将新的设备设计与改进的人-机器人控制相结合。它建立在我们与业界的强大合作伙伴关系之上，旨在开发安全、有效、可供普通NHS人群使用的下一代机器人。

项目成果

Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization.

• DOI: 10.1007/s11548-018-1743-5
• 发表时间: 2018-06
• 期刊: International journal of computer assisted radiology and surgery
• 影响因子: 3
• 作者: Chi W;Liu J;Rafii-Tari H;Riga C;Bicknell C;Yang GZ
• 通讯作者: Yang GZ

Learning-based Robotic Task Planning for Endovascular Catheterization

基于学习的血管内导管插入机器人任务规划

• 发表时间: 2017
• 作者: Chi W