We propose a new image reconstruction algorithm for CT, which is able to reduce the so-called metal artifact well. The most existing reconstruction algorithms for the metal artifact reduction consist of detecting metallic parts in the sinogram followed by image reconstruction after excluding or interpolating projection data corresponding to the identified metallic parts. However, the proposed algorithm consists of only a single computational step, leading to unifying the two steps into a single step. The proposed algorithm can be considered a particular application of Fault-Tolerant image reconstruction discovered by Kudo et al. [1]. The main idea is to use the L1 norm error Ax −b11 between Ax and b (x denotes image and b denotes projection data), or the error defined by using the Huber loss function Huber(Ax−b), instead of the ordinary L2 norm. The use of these robust error functions leads to excluding abnormal projection data passing through the metallic parts implicitly from the data fitting. The simulation result using a clinical dental CT image demonstrates that the proposed algorithm is able to reduce the metal artifact well by accurately identifying the location of metallic parts in the sinogram.
This paper proposes a new image reconstruction algorithm in sparse-view CT using the so-called nonlocal Total Variation (nonlocal TV) regularization. Compared to the previous work using the nonlocal TV, the proposed algorithm possesses the following three features. First, we introduce the newly developed modified nonlocal TV regularization term to preserve smooth intensity changes. Second, we utilize Passty’s proximal splitting framework to construct an accelerated iterative algorithm to minimize the cost function. Third, we introduce a novel technique called Selective Artifact Reduction (SAR) for further reduction of streak artifacts during the iteration. We demonstrate that the proposed algorithm can achieve significant image quality from 50-100 projection data with less than 20 iterations, through simulation studies using a clinical abdominal CT image.
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