For 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques which use X-ray fluoroscopic images and computer-aided design model of the knee implants, have been applied to clinical cases. These techniques are highly valuable for dynamic 3D kinematic analysis, but have needed time-consuming and labor-intensive manual operations in some process. In previous study, we reported a robust method to reduce manual operations to remove spurious edges and noises in edge detection process of X-ray images. In this study, we address another manual operations problem occurred when setting initial pose of TKA implants model for 2D/3D registration. To set appropriate initial pose of the model with manual operations for each X-ray image is important to obtain the good registration results. However, the number of X-ray images for a knee performance is very large, and thus to set initial pose with manual operations is very time-consuming and a problem for practical clinical applications. Therefore, this study proposes an initial pose estimation method for automated 3D kinematic analysis of TKA. 3D pose of an implant model is estimated using a 2D/3D registration technique based on a robust feature-based algorithm. To reduce labor-intensive manual operations of initial pose setting for large number of X-ray images, we utilize an interpolation technique with an approximate function. First, for some X-ray images (key frames), initial poses are manually adjusted to be as close as possible, and 3D poses of the model are accurately estimated for each key frame. These key frames were appropriately selected from the 2D feature point of knee motion in the X-ray images. Next, the 3D pose data estimated for each key frame are interpolated with an approximate function. In this study, we employed a multilevel B-spline function. Thus, we semi-automatically estimate the initial 3D pose of the implant model in X-ray images except for key frames. Fig. 1 shows the algorithm of initial pose estimation, and Fig. 2 shows the scheme of the data interpolation with an approximate function.Purpose
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To achieve 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques, which use X-ray fluoroscopic images and computer-aided design model of the knee implants, have been applied to clinical cases. In previous feature-based registration methods, only edge contours originated from knee implants are assumed to be extracted from X-ray images before 2D/3D registration. Due to the influence of bone and bone-cement close to knee implants, however, edge detection methods extract unwanted spurious edges and noises in clinical images. Thus, time-consuming and labor-intensive manual operations are often necessary to remove the unwanted edges. It has been a serious problem for clinical applications, and there is a strong demand for development of improved method. The purpose of this study was to develop a pose estimation method to perform accurate 2D/3D registration even if spurious edges and noises exist in knee images. Our 2D/3D registration technique is based on a feature-based algorithm, and contour points from X-ray images are extracted by Gaussian Laplacian filter and zero crossing methods. The basic principle of the algorithm is that the 3D pose of a model can be determined by projecting rays from contour points in an image back to the X-ray focus and noting that all of these rays are tangential to the model surface. Therefore, 3D poses are estimated by minimizing the sum of Euclidean distances between all projected rays and the model surface. Additionally, we introduce robust statistics into the 3D pose estimation method to perform accurate 2D/3D registration even if spurious edges and noises exist in knee images. The robust estimation method employs weight functions to reduce the influence of spurious edges and noises. The weight functions are defined for each contour point, and optimization is performed after the weight functions are multiplied to a cost function.Purpose
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