Mahfouz Mohamed R, Hoff William A, Komistek Richard D, Dennis Douglas A
Rocky Mountain Musculoskeletal Research Laboratory, Denver, CO 80222, USA.
IEEE Trans Med Imaging. 2003 Dec;22(12):1561-74. doi: 10.1109/TMI.2003.820027.
A method was developed for registering three-dimensional knee implant models to single plane X-ray fluoroscopy images. We use a direct image-to-image similarity measure, taking advantage of the speed of modern computer graphics workstations to quickly render simulated (predicted) images. As a result, the method does not require an accurate segmentation of the implant silhouette in the image (which can be prone to errors). A robust optimization algorithm (simulated annealing) is used that can escape local minima and find the global minimum (true solution). Although we focus on the analysis of total knee arthroplasty (TKA) in this paper, the method can be (and has been) applied to other implanted joints, including, but not limited to, hips, ankles, and temporomandibular joints. Convergence tests on an in vivo image show that the registration method can reliably find poses that are very close to the optimal (i.e., within 0.4 degrees and 0.1 mm), even from starting poses with large initial errors. However, the precision of translation measurement in the Z (out-of-plane) direction is not as good. We also show that the method is robust with respect to image noise and occlusions. However, a small amount of user supervision and intervention is necessary to detect cases when the optimization algorithm falls into a local minimum. Intervention is required less than 5% of the time when the initial starting pose is reasonably close to the correct answer, but up to 50% of the time when the initial starting pose is far away. Finally, extensive evaluations were performed on cadaver images to determine accuracy of relative pose measurement. Comparing against data derived from an optical sensor as a "gold standard," the overall root-mean-square error of the registration method was approximately 1.5 degrees and 0.65 mm (although Z translation error was higher). However, uncertainty in the optical sensor data may account for a large part of the observed error.
开发了一种将三维膝关节植入模型与单平面X射线荧光透视图像配准的方法。我们使用直接的图像到图像相似性度量，利用现代计算机图形工作站的速度快速渲染模拟（预测）图像。因此，该方法不需要在图像中对植入物轮廓进行精确分割（这可能容易出错）。使用了一种强大的优化算法（模拟退火），它可以避开局部最小值并找到全局最小值（真实解）。尽管我们在本文中专注于全膝关节置换术（TKA）的分析，但该方法可以（并且已经）应用于其他植入关节，包括但不限于髋关节、踝关节和颞下颌关节。对体内图像的收敛性测试表明，即使从具有较大初始误差的起始姿势开始，配准方法也能可靠地找到非常接近最优姿势（即偏差在0.4度和0.1毫米以内）。然而，Z（平面外）方向上平移测量的精度不太好。我们还表明该方法对图像噪声和遮挡具有鲁棒性。但是，当优化算法陷入局部最小值时，需要少量用户监督和干预来检测这种情况。当初始起始姿势合理地接近正确答案时，干预的时间不到5%，但当初始起始姿势远离正确答案时，干预时间高达50%。最后，对尸体图像进行了广泛评估，以确定相对姿势测量的准确性。与作为“金标准”的光学传感器获得的数据进行比较，配准方法的总体均方根误差约为1.5度和0.65毫米（尽管Z方向平移误差更高）。然而，光学传感器数据中的不确定性可能占观察到的误差的很大一部分。
