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Computed tomographic imaging of tissue surrounding metallic implants is often limited by metal artifacts. This paper compares 3 existing metal artifact reduction techniques that are based on segmentation of metal-affected regions in native images, followed by reprojection of segmented areas into original Radon space, removal of metal trace(s), and renewed reconstruction: Detector row-wise linear interpolation, 2-dimensional interpolation, and combination of row-wise linear interpolation and adaptive filtering. For each method, improvements of CT number accuracy and signal-noise as well as contrast-noise ratios near the prosthesis and in the image periphery over the values found for native images were evaluated in a phantom experiment simulating osteolytic bone lesions of different size and density around a Chrome-Cobalt hip prosthesis stem. Improvement in diagnostic usability was evaluated as lesion detectability by size. Quantitative and qualitative results showed that the linear interpolation and the combination method removed the artifacts most effectively. The mean accuracy error over different regions of interest placed in the direct vicinity of the metal and in the periphery of the object decreased 10-fold with linear interpolation. These methods increased contrast-noise ratio up to 68% of that measured on artifact-free images for the least dense lesion. Qualitatively, the linear interpolation and the combination method improved the lesion detectability and enabled differentiation of different lesion densities. However, in proximity to the stem, some artifacts remained for all methods. We conclude that published algorithms for metal artifact reduction substantially improve image quality for CT imaging of a metallic object and may be adequate for quantitative measurements except for the direct vicinity of the metallic object.