Metal artifact reduction in CT using fault-tolerant image reconstruction

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 −b ¹₁ 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.