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A robust optimization strategy for intensity-based 2D/3D registration of knee implant models to single-plane fluoroscopy

During intensity-based 2D/3D registration of 3D CAD models of knee implant components to a calibrated single-plane fluoroscopy image, a similarity measure between the fluoroscopy and a rendering of the model onto the image plane is maximized w.r.t. the 3D pose parameters of the model. This work focuses on a robust strategy for finding this maximum by extending the standard Powell optimization algorithm with problem-specific knowledge. A combination of feature-based and intensity-based methods is proposed. At each iteration of the optimization process, feature information is used to compute an additional search direction along which the image-based similarity measure is maximized. Hence, the advantages of intensity-based registration (accuracy) and feature-based registration (robustness) are combined. The proposed method is compared with the standard Powell optimization strategy, using an image-based similarity measure only, on simulated fluoroscopy images. It is shown that the proposed method generally has higher accuracy, more robustness and a better convergence behavior. Although introduced for the registration of 3D CAD models of knee implant components to single-plane fluoroscopy images, the optimization strategy is easily extendible and applicable to other 2D/3D registration applications.