Three-dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction

--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. EDICAL procedures such as diagnosis, surgical planning, and radiotherapy can be seriously degraded by the presence of metal artifacts in computed tomography (CT) imaging. Metal objects such as dental fillings, fixation devices, and other electric instruments implanted in patients' bodies inhibit X-ray propagation [1], preventing accurate calculation of the CT values during image reconstruction and yielding dark bands or streak artifacts in the CT images [2][3]. To correct the images, missing CT values for the underlying anatomical features must be compensated at the same time as the artifacts are removed. Although doctors make clinical efforts to manually collect such artifacts, this is a labor-intensive and time-consuming task. M. Nakao and T. Matsuda are with the Graduate School of Informatics, Kyoto University, Y oshida-Honmachi, Sakyo, Kyoto 606-8501, JAP AN; email: megumi@i.kyoto-u.ac.jp.