Loschak Paul M, Degirmenci Alperen, Howe Robert D
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138 USA.
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138 USA. Harvard - MIT Division of Health Sciences & Technology, Cambridge, MA 02139 USA.
IEEE Int Conf Robot Autom. 2017 May-Jun;2017:4830-4836. doi: 10.1109/ICRA.2017.7989561. Epub 2017 Jul 24.
Robotic cardiac catheterization using ultrasound (US) imaging catheters provides real time imaging from within the heart while reducing the difficulty in manually steering a four degree-of-freedom (4-DOF) catheter. Accurate robotic catheter navigation in the heart is challenging due to a variety of disturbances including cyclical physiological motions, such as respiration. In this work we compensate for respiratory motion by using an Extended Kalman Filter (EKF) to predict target motion and by applying the predictions to steer the US imaging catheter. The system performance was measured in bench top experiments with phantom vasculature. The robotic system with predictive filtering tracked cyclically moving targets with 1.59 mm and 0.72° mean error. Accurately tracking moving structures can improve intra-procedural treatments and visualization.
使用超声（US）成像导管的机器人心脏导管插入术可在心脏内部提供实时成像，同时降低手动操纵四自由度（4-DOF）导管的难度。由于包括呼吸等周期性生理运动在内的各种干扰，在心脏中进行精确的机器人导管导航具有挑战性。在这项工作中，我们通过使用扩展卡尔曼滤波器（EKF）来预测目标运动，并将预测结果应用于操纵超声成像导管，从而补偿呼吸运动。在具有模拟血管系统的台式实验中对系统性能进行了测量。具有预测滤波功能的机器人系统跟踪周期性移动目标的平均误差为1.59毫米和0.72°。准确跟踪移动结构可以改善术中治疗和可视化效果。
