An iterative reconstruction method for CT metal artifact reduction using L1 norm data fidelity and nonlocal TV regularization

Abstract

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 <i>L</i>₁ norm for data fidelity term and Nonlocal TV regularization. In ordinary iterative reconstruction for CT, the least-squares error || <i>A</i> ^&rarr;x - ^&rarr;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 ^&rarr;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 <i>L</i>₁norm error || <i>A</i> ^&rarr;x - ^&rarr;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.