膝关节运动实验仿真中人工数字化方法的验证。

Zachary Hargett, Manuel Gutierrez, M. Harman
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引用次数: 1

摘要

尸体试验是验证计算建模工作中使用的数学模型的常用方法。在计算全膝关节置换术(TKR)运动期间韧带张力的膝关节模型中,模型输入包括使用grood - sun坐标系定义的刚体运动,作为胫骨部件相对于股骨部件方向之间的空间联系。使用这种方法需要基于基准点定义每个刚性TKR组件(即胫骨和股骨)的坐标系,在实验设置中手动数字化点云,以及相对于相关骨标记阵列的方向注册。本研究的目的是比较两种不同的手动点数字化方法(在光学跟踪系统中校准的手持式触控笔和支点工具)之间的可变性,并以模拟尸体肢体实验装置中的TKR股骨组件为例。这是通过验证用于从数字化点计算坐标系统的数学算法,量化人工数字化方法的可变性,并讨论任何误差如何影响计算模型来实现的。手持式触控笔法计算原点和x、y、z轴的标准差分别为0.50mm、1.31°、0.51°和0.62°。重要的是要注意,使用手持式触控笔会产生额外的错误,因为需要对每个刚性标记阵列进行手动数字化。这个平均额外误差为0.54毫米原点和1.70,1.66和0.98度分别为x, y和z轴。对于枢轴工具方法,原点、x轴、y轴和z轴的标准差分别为0.35mm、0.37度、1.27度和1.24度。必须尽量减少实验误差,因为对准中的小误差会大大改变模型输出。在肢体运动的尸体模拟研究中,枢轴工具是最小化误差的较好选择。仔细定义基准点和可重复的点云人工数字化是有意义的基于尸体实验的TKR运动计算模型的关键。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VERIFICATION OF MANUAL DIGITIZATION METHODS DURING EXPERIMENTAL SIMULATION OF KNEE MOTION.
Cadaveric testing is a common approach for verifying mathematical models used in computational modeling work. In the case of a knee joint model for calculating ligament tension during total knee replacement (TKR) motion, model inputs include rigid body motions defined using the Grood-Suntay coordinate system as a spatial linkage between the tibial component orientation relative to the femoral component. Using this approach requires the definition of coordinate systems for each rigid TKR component (i.e. tibial and femoral) based on fiducial points, manual digitization of a point cloud within the experimental setup, and registration of the orientation relative to the relevant bone marker array. The purpose of this study was to compare the variability between two different manual point digitization methods (a hand-held stylus and pivot tool each calibrated in the optical tracking system), using a TKR femoral component in a simulated cadaver limb experimental setup as an example. This was accomplished by verifying the mathematical algorithm used to calculate the coordinate system from the digitized points, quantifying the variability of the manual digitization methods, and discussing how any error could affect the computational model. For the hand-held stylus method, the standard deviation of the origin and, x-, y-, and z-axis calculations were 0.50mm, 1.31 degrees, 0.51 degrees, and 0.62 degrees, respectively. It is important to note that there is an additional error created using the hand-held stylus from required manual digitization of each rigid marker array. This average additional error was 0.54mm for the origin and 1.70, 1.66, and 0.98 degrees for the x-, y-, and z-axes, respectively. For the pivot tool method, the standard deviation was 0.35mm, 0.37 degrees, 1.27 degrees, and 1.24 degrees for the origin, x-, y-, and z-axes, respectively. It is essential to minimize experimental error, as small errors in alignment can substantially alter model outputs. In this study of cadaver simulation of limb motion, the pivot tool is the better option for minimizing error. Careful definition of fiducial points and repeatable manual digitization of the point cloud is critical for meaningful computational models of TKR motion based on cadaver experimental work.
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