  The reduction of metal artifacts in computed tomography (CT) images,
specifically for strong artifacts generated from multiple metal objects, is a
challenging issue in medical imaging research. Although there have been some
studies on supervised metal artifact reduction through the learning of
synthesized artifacts, it is difficult for simulated artifacts to cover the
complexity of the real physical phenomena that may be observed in X-ray
propagation. In this paper, we introduce metal artifact reduction methods based
on an unsupervised volume-to-volume translation learned from clinical CT
images. We construct three-dimensional adversarial nets with a regularized loss
function designed for metal artifacts from multiple dental fillings. The
results of experiments using 915 CT volumes from real patients demonstrate that
the proposed framework has an outstanding capacity to reduce strong artifacts
and to recover underlying missing voxels, while preserving the anatomical
features of soft tissues and tooth structures from the original images.
