Clinical Research Progress on Deep Learning for Metal Artifact Reduction in Computed Tomography Images
LIU Xiangcheng
HUANG Xiaoying
WEI Pengyue
WANG Pengchao
GUO Fangkai
BAO Yunfeng
Abstract：Computed tomography(CT)examination is the preferred method for the postoperative evaluation of patients with metal implants.However,some metal implants may produce artifacts in CT images,thus compromising the visualization of both the metal implants themselves and surrounding soft tissues.Furthermore,this can affect clinical diagnosis and treatment accuracy.In recent years,rapid developments in artificial intelligence have enabled deep learning reconstruction algorithms that have offered new solutions for reducing metal artifacts and have shown great promise.This paper reviews the research progress of deep learning reconstruction algorithms in reducing metal artifacts,with the aim of reducing a impact and support accurate clinical diagnosis and treatment.
Keywords：CT imagingartificial intelligencedeep learningimage reconstruction algorithmmetal artifact reduction
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：8( 28-35 )