Projection Domain Metal Artifact Reduction in Computed Tomography using Conditional Generative Adversarial Networks

High-density objects in the field of view, still remain one of the major challenges in CT image reconstruction. They cause artifacts in the image, which degrade the quality and the diagnostic value of the image. Standard approaches for metal artifact reduction are often unable to correct these artifacts sufficiently or introduce new artifacts. In this work, a new deep learning approach for the reduction of metal artifacts in CT images is proposed using a Generative Adversarial Network. A generator network is applied directly to the projection data corrupted by the metal objects to learn the corrected data. In addition, a second network, the discriminator, is used to evaluate the quality of the learned data. The results of the trained generator network show that most of the data could be reasonably replaced by the network, reducing the artifacts in the reconstructed image.