Robust Estimation of Knee Kinematics After Total Knee Arthroplasty with Evolutional Computing Approach

Analyzing knee kinematics after total knee arthroplasty (TKA) has been attracting considerable attentions because the knee kinematics can be used to evaluate TKA patients and to evaluate TKA operations and design of knee implants. Knee kinematics can be estimated by 2-D/3-D image registration from 3-D computer-aided design (CAD) models of knee implants to 2-D X-ray image. Although there are many studies for estimating knee kinematics, they have common problems that are dependency on initial pose/position and falling into local maxima. This study proposes a robust 2-D/3-D image registration method based on evolutional computing. The evolutional computing has both characteristics of global search performance and of local search performance. The characteristics are suitable for solving the problems of 2-D/3-D image registration. The proposed system has been evaluated by applying it to computer-synthesized images, X-ray images of phantoms, and X-ray images of TKA patients.