Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, Canada.
Cancer Centre of Southeastern Ontario, Kingston General Hospital, Kingston, Canada.
Int J Comput Assist Radiol Surg. 2017 Apr;12(4):681-689. doi: 10.1007/s11548-017-1534-4. Epub 2017 Feb 18.
Electromagnetic (EM) catheter tracking has recently been introduced in order to enable prompt and uncomplicated reconstruction of catheter paths in various clinical interventions. However, EM tracking is prone to measurement errors which can compromise the outcome of the procedure. Minimizing catheter tracking errors is therefore paramount to improve the path reconstruction accuracy.
An extended Kalman filter (EKF) was employed to combine the nonlinear kinematic model of an EM sensor inside the catheter, with both its position and orientation measurements. The formulation of the kinematic model was based on the nonholonomic motion constraints of the EM sensor inside the catheter. Experimental verification was carried out in a clinical HDR suite. Ten catheters were inserted with mean curvatures varying from 0 to [Formula: see text] in a phantom. A miniaturized Ascension (Burlington, Vermont, USA) trakSTAR EM sensor (model 55) was threaded within each catheter at various speeds ranging from 7.4 to [Formula: see text]. The nonholonomic EKF was applied on the tracking data in order to statistically improve the EM tracking accuracy. A sample reconstruction error was defined at each point as the Euclidean distance between the estimated EM measurement and its corresponding ground truth. A path reconstruction accuracy was defined as the root mean square of the sample reconstruction errors, while the path reconstruction precision was defined as the standard deviation of these sample reconstruction errors. The impacts of sensor velocity and path curvature on the nonholonomic EKF method were determined. Finally, the nonholonomic EKF catheter path reconstructions were compared with the reconstructions provided by the manufacturer's filters under default settings, namely the AC wide notch and the DC adaptive filter.
With a path reconstruction accuracy of 1.9 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (2.4 mm) by 21% and the raw EM measurements (3.5 mm) by 46%. Similarly, with a path reconstruction precision of 0.8 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (1.0 mm) by 20% and the raw EM measurements (1.7 mm) by 53%. Path reconstruction accuracies did not follow an apparent trend when varying the path curvature and sensor velocity; instead, reconstruction accuracies were predominantly impacted by the position of the EM field transmitter ([Formula: see text]).
The advanced nonholonomic EKF is effective in reducing EM measurement errors when reconstructing catheter paths, is robust to path curvature and sensor speed, and runs in real time. Our approach is promising for a plurality of clinical procedures requiring catheter reconstructions, such as cardiovascular interventions, pulmonary applications (Bender et al. in medical image computing and computer-assisted intervention-MICCAI 99. Springer, Berlin, pp 981-989, 1999), and brachytherapy.
为了能够在各种临床介入中快速、简便地重建导管路径,最近引入了电磁(EM)导管跟踪技术。然而,EM 跟踪容易出现测量误差,这可能会影响手术的结果。因此,最大限度地减少导管跟踪误差对于提高路径重建精度至关重要。
采用扩展卡尔曼滤波器(EKF)将 EM 传感器在导管内的非线性运动模型与位置和方向测量值相结合。运动模型的公式基于 EM 传感器在导管内的非完整运动约束。在临床 HDR 套件中进行了实验验证。在模型中,将十种导管以 0 到 [公式:见正文] 的平均曲率插入到体模中。在各种速度下(速度范围为 7.4 到 [公式:见正文]),在每个导管内插入一个微型 Ascension(伯灵顿,佛蒙特州,美国)trakSTAR EM 传感器(型号 55)。应用非完整 EKF 对跟踪数据进行统计分析,以提高 EM 跟踪的准确性。在每个点上定义了一个样本重建误差,即估计的 EM 测量值与其对应的真实值之间的欧几里得距离。路径重建精度定义为样本重建误差的均方根,而路径重建精度定义为这些样本重建误差的标准差。确定了传感器速度和路径曲率对非完整 EKF 方法的影响。最后,将非完整 EKF 导管路径重建与制造商提供的默认设置下的滤波器(即 AC 宽陷波滤波器和 DC 自适应滤波器)的重建进行了比较。
非完整 EKF 的路径重建精度为 1.9 毫米,比制造商的滤波器(2.4 毫米)提高了 21%,比原始 EM 测量值(3.5 毫米)提高了 46%。同样,非完整 EKF 的路径重建精度为 0.8 毫米,比制造商的滤波器(1.0 毫米)提高了 20%,比原始 EM 测量值(1.7 毫米)提高了 53%。当改变路径曲率和传感器速度时,路径重建精度没有明显的趋势;相反,重建精度主要受 EM 场发射器位置的影响([公式:见正文])。
先进的非完整 EKF 在重建导管路径时,能够有效地减少 EM 测量误差,对路径曲率和传感器速度具有鲁棒性,并且可以实时运行。我们的方法有望应用于多种需要导管重建的临床程序,如心血管介入、肺部应用(Bender 等人在医学图像计算和计算机辅助干预-MICCAI 99. Springer,柏林,第 981-989 页,1999 年)和近距离放射治疗。