Three-dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction

TL;DR

This paper introduces a 3D unsupervised generative adversarial network approach for reducing metal artifacts in CT images, effectively handling complex real-world artifacts without relying on simulated data.

Contribution

It presents a novel 3D adversarial network framework for unsupervised metal artifact reduction directly learned from clinical CT images, improving artifact removal and tissue preservation.

Findings

Effective reduction of strong metal artifacts in real patient CT scans.

Superior preservation of soft tissue and tooth structures.

Outperforms existing methods on a dataset of 915 CT volumes.

Abstract

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.