器官挂载机器人的生理运动建模。
Physiological motion modeling for organ-mounted robots.
机构信息
The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Cardiovascular Institute, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
出版信息
Int J Med Robot. 2017 Dec;13(4). doi: 10.1002/rcs.1805. Epub 2017 Feb 17.
BACKGROUND
Organ-mounted robots passively compensate heartbeat and respiratory motion. In model-guided procedures, this motion can be a significant source of information that can be used to aid in localization or to add dynamic information to static preoperative maps.
METHODS
Models for estimating periodic motion are proposed for both position and orientation. These models are then tested on animal data and optimal orders are identified. Finally, methods for online identification are demonstrated.
RESULTS
Models using exponential coordinates and Euler-angle parameterizations are as accurate as models using quaternion representations, yet require a quarter fewer parameters. Models which incorporate more than four cardiac or three respiration harmonics are no more accurate. Finally, online methods estimate model parameters as accurately as offline methods within three respiration cycles.
CONCLUSIONS
These methods provide a complete framework for accurately modelling the periodic deformation of points anywhere on the surface of the heart in a closed chest.
背景
器官安装的机器人可以被动补偿心跳和呼吸运动。在模型引导的程序中，这种运动可能是一个重要的信息来源，可以用于辅助定位或为静态术前图谱添加动态信息。
方法
针对位置和方向，提出了用于估计周期性运动的模型。然后，在动物数据上对这些模型进行了测试，并确定了最佳阶数。最后，展示了在线识别方法。
结果
使用指数坐标和 Euler 角参数化的模型与使用四元数表示的模型一样准确，但需要的参数少四分之一。包含超过四个心脏或三个呼吸谐频的模型并没有更准确。最后，在线方法在三个呼吸周期内可以像离线方法一样准确地估计模型参数。
结论
这些方法为在闭胸条件下准确地对心脏表面任何位置的周期性变形点建模提供了完整的框架。
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