A common complication associated with hip arthoplasty is prosthesis migration, and for most cemented components a migration greater than 0.85 mm within the first six months after surgery, are an indicator for prosthesis failure. Currently, prosthesis migration is evaluated using X-ray images, which can only reliably estimate migrations larger than 5 mm. We propose an automated method for estimating prosthesis migration more accurately, using CT images and image registration techniques. We report on the results obtained using an experimental set-up, in which a metal prosthesis can be translated and rotated with respect to a cadaver femur, over distances and angles applied using a combination of positioning stages. Images are first preprocessed to reduce artefacts. Bone and prosthesis are extracted using consecutive thresholding and morphological operations. Two registrations are performed, one aligning the bones and the other aligning the prostheses. The migration is estimated as the difference between the found transformations. We use a robust, multi-resolution, stochastic optimization approach, and compare the mean squared intensity differences (MS) to mutual information (MI). 30 high-resolution helical CT scans were acquired for prosthesis translations ranging from 0.05 mm to 4 mm, and rotations ranging from 0.3° to 3° . For the translations, the mean 3D registration error was found to be 0.22 mm for MS, and 0.15 mm for MI. For the rotations, the standard deviation of the estimation error was 0.18° for MS, and 0.08° for MI. The results show that the proposed approach is feasible and that clinically acceptable accuracies can be obtained. Clinical validation studies on patient images will now be undertaken.
Image reconstruction from truncated tomographic data is an important practical problem in CT in order to reduce the X-ray dose and to improve the resolution. The main problem with the Radon Transform is that in 2D the inversion formula globally depends upon line integrals of the object function. The standard Filtered Backprojection algorithm (FBP) does not allow any type of truncation. A typical strategy is to extrapolate the truncated projections with a smooth 1D function in order to reduce the discontinuity artefacts. The low-frequency artifact reduction however, severely depends upon the width of the extrapolation, which is unknown in practice. In this paper we develop a modified ConTraSPECT-type method for specific use on truncated 2D CT-data, when only a local area (ROI) is to be imaged. The algorithm describes the shape and structure of the region surrounding the ROI by a specific object with only few parameters, in this paper a uniform ellipse. The parameters of this ellipse are optimized by minimizing the Helgason-Ludwig consistency conditions for the sinogram completed with Radon data of the ellipse. Simulations show that the MSE of the reconstructions is reduced significantly, depending on the type of truncation.
In this paper, we develop a new algorithm that enables the reconstruction of a region of interest (ROI) in X-ray Computed Tomography (CT), in case only a local region of the object is to be imaged. The method uses a Gaussian window function in order to reduce the X-ray attenuation from the region outside the ROI. The method uses almost completely local data and reduces the amount of exposure significantly. Many algorithms can be easily combined with our algorithm in order to improve the reconstruction quality. The main goal of this work is to reduce the bias in order to allow quantitative analysis of the CT-images.