This paper deals with the so-called Metal Artifact Reduction (MAR) in CT. This problem aims at reconstructing a CT image with reduced metal induced artifact when the object contains metallic parts inside. We propose a new iterative reconstruction method to the MAR problem, which uses the L1 norm for data fidelity term and Nonlocal TV regularization. In ordinary iterative reconstruction for CT, the least-squares error || A→x - →b|| 22 Is used as data fidelity term for image reconstruction. However, it is well-known that the least-squares criterion is sensitive to the existence of abnormal (inconsistent) data in the measurement →b, such as projection data passing through the metallic parts in this work. A simple reasonable method to identify the location of metallic parts in the sinogram and exclude the corresponding projection data from the data fitting is to use the L1 norm error || A→x - →b|| 11 . Furthermore, the power of proposed method to reduce the metal artifact can be significantly improved by adding Nonlocal Total Variation (NLTV) regularization term into the cost function. Compared to existing approaches to the MAR problem, the proposed method possesses the following attractive feature. Almost every approach to MAR consists of two-step computations. The first step detects the metallic parts in the sinogram and the second step performs image reconstruction after interpolating or excluding the projection data corresponding to the identified metallic parts. On the other hand, the proposed method consists of only a single computational step, i.e. single iterative minimization of a convex cost function, leading to smartly unifying the two steps into a single step.
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.
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.
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