Department of Medical Biophysics, The University of Western Ontario, and the Robarts Research Institute, London, ON, N2V 1C5, Canada.
IEEE Trans Med Imaging. 2010 Mar;29(3):879-94. doi: 10.1109/TMI.2009.2039344.
In recent years, magnetic tracking systems, whose fundamental unit of measurement is a 5D transformation (three translational and two rotational degrees-of-freedom), have become much more popular. Two 5D sensors can be combined to obtain a 6D transformation similar to the ones provided by the point-based registration in optical tracking. However, estimates of the tool tip uncertainty, which we have called the target tracking error (TTE) since no registration is explicitly performed, are not available in the same manner as their optical counterpart. If the systematic bias error can be corrected and estimates of the 5D or 6D fiducial localizer error (FLE) are provided in the form of zero mean normally distributed random variables in [Formula: see text] and [Formula: see text], respectively, then the TTE can be modeled. In this paper, the required expressions that model the TTE as a function of the systematic bias, FLE and target location are derived and then validated using Monte Carlo simulations. We also show that the first order approximation is sufficient beyond the range of errors typically observed during an image-guided surgery (IGS) procedure. Applications of the models are described for a minimally invasive intracardiac surgical guidance system and needle-based therapy systems. Together with the target registration error (TRE) statistical models for point-based registration, the models presented in this article provide the basic framework for estimating the total system measurement uncertainty for an IGS system. Future work includes developing TRE models for commonly used registration methods that do not already have them.
近年来,磁性跟踪系统越来越受欢迎,其基本测量单位是 5D 变换(三个平移自由度和两个旋转自由度)。两个 5D 传感器可以组合在一起,获得类似于基于点的光学跟踪注册提供的 6D 变换。然而,与光学跟踪相比,无法以相同的方式获得工具尖端不确定性的估计值,我们称之为目标跟踪误差(TTE),因为没有明确执行注册。如果可以纠正系统偏差误差,并且可以以零均值正态分布的随机变量形式分别在 [公式:见文本] 和 [公式:见文本] 中提供 5D 或 6D 基准定位器误差(FLE)的估计值,那么就可以对 TTE 进行建模。本文推导了将 TTE 建模为系统偏差、FLE 和目标位置函数的所需表达式,然后使用蒙特卡罗模拟进行了验证。我们还表明,在图像引导手术(IGS)过程中通常观察到的误差范围内,一阶近似是足够的。模型的应用描述了用于微创心脏内手术引导系统和基于针的治疗系统。与基于点的注册的目标注册误差(TRE)统计模型一起,本文提出的模型为估计 IGS 系统的总系统测量不确定性提供了基本框架。未来的工作包括为尚未具有 TRE 模型的常用注册方法开发 TRE 模型。