Three-dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction
This addresses a challenging issue in medical imaging for clinicians and patients, offering an unsupervised approach to improve CT image quality, though it is incremental as it builds on existing GAN methods for artifact reduction.
The paper tackled the problem of reducing strong metal artifacts in CT images from multiple dental fillings by proposing an unsupervised volume-to-volume translation method using 3D generative adversarial nets, achieving outstanding artifact reduction and recovery of missing voxels while preserving anatomical features, as demonstrated on 915 real patient CT volumes.
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.