  Computed tomography (CT) images containing metallic objects commonly show
severe streaking and shadow artifacts. Metal artifacts are caused by nonlinear
beam-hardening effects combined with other factors such as scatter and Poisson
noise. In this paper, we propose an implant-specific method that extracts
beam-hardening artifacts from CT images without affecting the background image.
We found that in cases where metal is inserted in the water (or tissue), the
generated beam-hardening artifacts can be approximately extracted by
subtracting artifacts generated exclusively by metals. We used a deep learning
technique to train nonlinear representations of beam-hardening artifacts
arising from metals, which appear as shadows and streaking artifacts. The
proposed network is not designed to identify ground-truth CT images (i.e., the
CT image before its corruption by metal artifacts). Consequently, these images
are not required for training. The proposed method was tested on a dataset
consisting of real CT scans of pelvises containing simulated hip prostheses.
The results demonstrate that the proposed deep learning method successfully
extracts both shadowing and streaking artifacts.
