Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization.
Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization.
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DOI：
10.1007/s11548-018-1743-5
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发表时间：
2018-06
影响因子：
3
通讯作者：
Yang GZ
中科院分区：
文献类型：
--
作者：
Chi W;Liu J;Rafii-Tari H;Riga C;Bicknell C;Yang GZ
在存在复杂的解剖学的情况下，内血管内的干预措施受到二维术中成像的限制。导管平台基于以前的工作（Rafi-Tari等人，在：Miccai2013，pp 369–377。https：//doi.org/10.1007/978-3-642-40763-5_46，）通过提出一种方法来解决驱动器的方法。患者特异性解剖学之间的尺度和方向差异。
实施统计模型以编码近端的导管运动，并根据专家算子从单个幻影获得的插管数据来区分站点。进行了交叉验证，以测试由机器人平台执行的插管任务的成功率。
在干燥模拟的情况下，半自动插管的成功率为98.1％，在连续流动模拟下，拟议的机器人方法与手动方法相比，使用机器人方法可以降低33.3％。
这项工作提供了有关导管任务计划的见解，并改进了动手人体工程学导管导航机器人的设计。
Endovascular intervention is limited by two-dimensional intraoperative imaging and prolonged procedure times in the presence of complex anatomies. Robotic catheter technology could offer benefits such as reduced radiation exposure to the clinician and improved intravascular navigation. Incorporating three-dimensional preoperative imaging into a semiautonomous robotic catheterization platform has the potential for safer and more precise navigation. This paper discusses a semiautonomous robotic catheter platform based on previous work (Rafii-Tari et al., in: MICCAI2013, pp 369–377. https://doi.org/10.1007/978-3-642-40763-5_46,) by proposing a method to address anatomical variability among aortic arches. It incorporates anatomical information in the process of catheter trajectories optimization, hence can adapt to the scale and orientation differences among patient-specific anatomies. Statistical modeling is implemented to encode the catheter motions of both proximal and distal sites based on cannulation data obtained from a single phantom by an expert operator. Non-rigid registration is applied to obtain a warping function to map catheter tip trajectories into other anatomically similar but shape/scale/orientation different models. The remapped trajectories were used to generate robot trajectories to conduct a collaborative cannulation task under flow simulations. Cross-validations were performed to test the performance of the non-rigid registration. Success rates of the cannulation task executed by the robotic platform were measured. The quality of the catheterization was also assessed using performance metrics for manual and robotic approaches. Furthermore, the contact forces between the instruments and the phantoms were measured and compared for both approaches. The success rate for semiautomatic cannulation is 98.1% under dry simulation and 94.4% under continuous flow simulation. The proposed robotic approach achieved smoother catheter paths than manual approach. The mean contact forces have been reduced by 33.3% with the robotic approach, and 70.6% less STDEV forces were observed with the robot. This work provides insights into catheter task planning and an improved design of hands-on ergonomic catheter navigation robots.
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作者：
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发表时间：
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影响因子：
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发表时间：
2010-12-01
影响因子：
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作者：
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