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Combined registration of 3D tibia and femur implant models in 3D magnetic resonance images

机译:3D磁共振图像中3D胫骨和股骨植入物模型的组合配准

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The most frequent reasons for revision of total knee arthroplasty are loosening and abnormal axial alignment leading to an unphysiological kinematic of the knee implant. To get an idea about the postoperative kinematic of the implant, it is essential to determine the position and orientation of the tibial and femoral prosthesis. Therefore we developed a registration method for fitting 3D CAD-models of knee joint prostheses into an 3D MR image. This rigid registration is the basis for a quantitative analysis of the kinematics of knee implants. Firstly the surface data of the prostheses models are converted into a voxel representation; a recursive algorithm determines all boundary voxels of the original triangular surface data. Secondly an initial preconfiguration of the implants by the user is still necessary for the following step: The user has to perform a rough preconfiguration of both remaining prostheses models, so that the fine matching process gets a reasonable starting point. After that an automated gradient-based fine matching process determines the best absolute position and orientation: This iterative process changes all 6 parameters (3 rotational- and 3 translational parameters) of a model by a minimal amount until a maximum value of the matching function is reached. To examine the spread of the final solutions of the registration, the interobserver variability was measured in a group of testers. This variability, calculated by the relative standard deviation, improved from about 50% (pure manual registration) to 0.5% (rough manual preconfiguration and subsequent fine registration with the automatic fine matching process).
机译:修改全膝关节形成术的最常见原因是松动和异常的轴向对准,导致膝关节植入物的失利运动。为了了解植入物的术后运动学,必须确定胫骨和股骨假体的位置和取向。因此,我们开发了一种注册方法,用于将3D CAD模型的膝关节假体拟合到3D MR图像中。这种刚性登记是膝关节植入物运动学进行定量分析的基础。首先,假体模型的表面数据被转换为体素表示;递归算法确定原始三角形表面数据的所有边界体素。其次,用户仍然需要对植入物的初始预配置仍然需要进行以下步骤:用户必须对剩余的假体模型进行粗糙的预先配置,使得精细匹配过程获得合理的起点。之后,在基于自动梯度的精细匹配过程确定最佳的绝对位置和方向:该迭代过程将模型的所有6个参数(3个旋转 - 和3个平移参数)更新,直到匹配函数的最大值是最大值到达。为了检查登记的最终解决方案的传播,在一组测试仪中测量了interobserver变异性。这种可变性,通过相对标准偏差计算,从约50%(纯手动注册)提高到0.5%(粗略手动预配置和随后与自动精细匹配过程的精细配准)。

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