CT Image Metal Artifact Reduction Based on Deep Learning
YE Zihao
JIN Tong
CHE Zigang
WANG Shisen
LIU Jin
CHEN Yang
Abstract：Metal artifacts adversely affect computed tomography(CT)image quality and diagnostic accuracy.Metal-artifact reduction(MAR)in CT images has long been a major focus of research.In recent years,with the advancement and application of deep-learning technologies,new approaches have emerged for research on MAR algorithms,leading to a wealth of outstanding achievements.In this paper,we first introduce the causes and manifestations of metal artifacts in CT images.We then review recent progress in deep-learning-based MAR methods,categorizing them into three approaches:image,projection,and dual domains.Finally,we summarize these methods and discuss future research prospects for MAR technology.
Keywords：CT imagemetal artifact reductiondeep learningdual domainunsupervised learning
Publication Date：2026-01-31
Online Publishing Date：2026-03-06(First online date of this platform, not the publication date of the document)
Pages：13( 15-27 )