A priori knowledge based particle filter for estimating 3-D pose position of implanted knee.
ABSTRACT For estimating 3-D pose position of artificial knee implants in vivo, there are some studies based on 2-D/3-D image registration of 2-D fluoroscopy images and 3-D geometrical model. Knee implant mainly consists of femoral component and tibial component. Most conventional studies estimate 3-D pose position of femoral component and tibial component individually. Rather, they don't evaluate relative position between the femoral and tibial components. This paper proposes a method for estimating 3-D pose position of implanted knee based on particle filter. A priori knowledge on the relational position of the components are utilized by using fuzzy membership functions. The experimental results for a patient and simulation DR images showed that the proposed method adequately estimate 3-D pose position of the femoral and tibial components with respect to relational position between the components.
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ABSTRACT: 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.IEEE Transactions on Medical Imaging 01/2004; 22(12):1561-74. · 4.03 Impact Factor
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ABSTRACT: The experimental study of joint kinematics in three dimensions requires the description and measurement of six motion components. An important aspect of any method of description is the ease with which it is communicated to those who use the data. This paper presents a joint coordinate system that provides a simple geometric description of the three-dimensional rotational and translational motion between two rigid bodies. The coordinate system is applied to the knee and related to the commonly used clinical terms for knee joint motion. A convenient characteristic of the coordinate system shared by spatial linkages is that large joint displacements are independent of the order in which the component translations and rotations occur.Journal of Biomechanical Engineering 06/1983; 105(2):136-44. · 1.52 Impact Factor
- JACIII. 01/2005; 9:181-195.