Regularized Three-Dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction in Head and Neck CT Images

Megumi Nakao,Nobuhiro Ueda,Keiho Imanishi,Tadaaki Kirita,Tetsuya Matsuda,Yuichiro Imai

doi:10.1109/access.2020.3002090

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.

Highlights

Medical procedures such as diagnosis, surgical planning, and radiotherapy can be seriously degraded by the presence of metal artifacts in computed tomography (CT) imaging
We introduce metal artifact reduction (MAR) methods based on volume-to-volume translation learned from unpaired clinical CT images
The results show that 3DGAN removed most parts of the appliances and associated metal artifacts while preserving 3D teeth structures

Summary

Introduction

Medical 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]. Many researchers have studied image filtering or reconstruction methods [2], [4]–[7], but metal artifact reduction (MAR) remains a challenging.

Methods

Results

Discussion

Conclusion