Unsupervised Reused Convolutional Network for Metal Artifact Reduction

Binyu Zhao, Jinbao Li, Qianqian Ren, Yingli Zhong

Abstract: Nowadays computed tomography (CT) is widely used for medical diagnosis and treatment. However, CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which could affect the quality of CT images and increase the possibility of false diagnosis and analysis. Recently, Convolutional Neural Network (CNN) was applied for metal artifact reduction (MAR) with synthesized paired images, which is not accurate enough to simulate the mechanism of imaging. With unpaired images, the first unsupervised model ADN appeared. But it is complicated in architecture and has distance to reach the level of existing supervised methods. To narrow the gap between unsupervised methods with supervised methods, this paper introduced a simpler multi-phase deep learning method extracting features recurrently to generate both metal artifacts and non-artifact images. Artifact Generative Network and Image Generative Network are presented jointly to remove metal artifacts. Extensive experiments show a better performance than ADN on synthesized data and clinical data.