Purpose

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. However, most conventional methods have needed time-consuming and labor-intensive manual operations in some process. In particular, for the 3D pose estimation of tibial component model from X-ray images, these manual operations were carefully performed because the pose estimation of symmetrical tibial component get severe local minima rather than that of unsymmetrical femoral component. In this study, therefore, we propose an automated 3D kinematic estimation method of tibial component based on statistical motion model, which is created from previous analyzed 3D kinematic data of TKA.

To achieve 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques which use X-ray fluoroscopic images and computer-aided design (CAD) model of the knee implants, have been applied to clinical cases. These techniques are highly valuable for dynamic 3D kinematic measurement of TKA implants, but have needed time-consuming and labor-intensive manual operations in some process. To overcome a manual operations problem of initial pose estimation for 2D/3D registration, this study proposes an improvement method for semi-automated 3D kinematic measurement of TKA using X-ray fluoroscopic images.

To automatically estimate the initial pose of the implant CAD model, we utilise a transformation with feature points extracted from the previous and next frames. A transform matrix which has three degree of freedom (translations parallel to the image, and a rotation perpendicular to the image) is calculated by registration of corresponding feature points between the previous and next frame extracted with speeded up robust features (SURF) algorithm. While, the corresponding point sets extracted by SURF sometimes include some error sets. Therefore, in this study, least median of squares method is employed to detect the error corresponding sets and calculate a transform matrix accurately. Finally, the 3D pose of the model estimated (by the 2D/3D registration) in previous frame is transformed with the accurately calculated transform matrix, and the transformed pose is used as an initial 3D pose of the model (for the 2D/3D registration) in next frame.

To validate the feasibility of the improved semi-automated 3D kinematic measurement method, experiments using X-ray fluoroscopic images of four TKA patients during knee motions were performed. In order to assess the performance of the improved method, automation rate was calculated, and the rate was defined as the X-ray frame number of satisfying clinical required accuracy (error within 1mm, 1 degree) relative to all X-ray frame number. As results of the experiments, 3D pose of the model for all X-ray images except for the first frame is automatically stably-estimated, the automation rate of the femoral and tibial component were 83.7 % and 73.5 %, respectively.

The improved method doesn't need labor-intensive manual operations for 3D kinematic measurement of TKA, and is thought to be very helpful for actual clinical practice.

Purpose:

To materialize 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques, which use X-ray fluoroscopic images and the knee implants CAD, have been applied to clinical cases. However, most conventional methods have needed time-consuming and labor-intensive manual operations in some process. In previous study, we addressed a manual operations problem when setting initial pose of implants model for 2D/3D registration, and reported a semi-automated initial pose estimation method based on an interpolation technique. However, this method still required appropriate initial pose estimation of the model with manual operations for some X-ray images (key frames). Additionally, in the situation like fast knee motion and use of low frame rate, good registration results were not obtained because of the large displacement between each frame silhouette. To overcome these problems, this study proposes an improved semi-automated 3D kinematic estimation method.