Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4566-4569. doi: 10.1109/EMBC46164.2021.9630917.
One of the critical components of robotic-assisted beating heart surgery is precise localization of a point-of-interest (POI) position on cardiac surface, which needs to be tracked by the robotic instruments. This is challenging as the incoming sensor measurements, from which POI position is localized, might be noisy and incomplete. This paper presents two Bayesian filtering based localization approaches to localize POI position online from sonomicrometer measurements. Specifically, extended Kalman filter (EKF) and particle filter (PF) localization algorithms are explored to estimate the state of POI position. The estimations of upcoming heart motion generated by the generalized adaptive predictor, which is demonstrated in the authors' past work, are also incorporated to generate an improved motion model. The proposed methods are validated with prerecorded in-vivo heart motion data.
在机器人辅助心脏不停跳手术中，一个关键的组成部分是精确地定位心脏表面的兴趣点（POI）位置，这需要由机器人器械进行跟踪。这是具有挑战性的，因为从 POI 位置定位的传入传感器测量值可能是嘈杂和不完整的。本文提出了两种基于贝叶斯滤波的定位方法，从超声心动图测量中在线定位 POI 位置。具体来说，扩展卡尔曼滤波器（EKF）和粒子滤波器（PF）定位算法被探索来估计 POI 位置的状态。由广义自适应预测器生成的即将到来的心脏运动的估计值，这在作者过去的工作中已经得到了证明，也被纳入生成改进的运动模型。所提出的方法通过预先记录的体内心脏运动数据进行验证。
