X-ray CT (Computed Tomography) is a technique that can generate cross-sectional images of an object. This technology has spread rapidly in the field of diagnosis and inspection, and have provided significant benefits for our lives. However, when metal objects are present in the X-ray projection, a strong radial noise called metal artifacts arises and makes it difficult to diagnose and inspect. Even though various studies to reduce metal artifacts have been conducted, the problem is not resolved yet. In order to reduce these artifacts, we have proposed new algorithms based on the FBP(Filtered back projection), which remains the most widely used reconstruction method in CT. On the other hand, iterative reconstruction (IR) has emerged as a new CT reconstruction technique in recent years. IR requires a lot of computation time, however, it enables reduction in pixel noise or artifacts. In this paper, we proposed a new reconstruction algorithm to reduce metal artifacts based on IR. The algorithm utilizes the energy information to apply the exact successive approximation. Validation was performed using a simple experimental phantom. Our results demonstrate that the new algorithm effectively reduces metal artifacts in certain situations.Key Words: X-ray CT, nondestructive inspection, image processing, metal artifact, iterative reconstruction
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X-ray CT is one of the most important diagnostic techniques which can generate cross-sectional images of an object. However, X-ray CT is still facing some technical issues; in particular, metal artifacts are a significant problem in CT imaging. A metal artifact is a radial noise caused by a discrepancy of projection data, and makes it difficult to diagnose patients with metal implants in their body. Even though various studies to reduce metal artifacts were conducted, the problem is not resolved yet. Therefore, the purpose of this study is to propose a method to reduce metal artifact, so as to widely disseminate a superior diagnostic technology. First, we propose a method for two dimensional CT data in this paper. The proposed method is divided into three processes, metal extraction, non-metal interpolation, and synthesizing. The algorithm is highly effective in metal artifact reduction because it is not sensitive to the discrepancy of projection data. And then, we extend the method to three dimensional CT data, and evaluate the effectiveness.
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