CT理论与应用研究2026，Vol.35Issue(1)：28-35,8.DOI:10.15953/j.ctta.2025.222
深度学习去CT图像金属伪影的临床研究进展
Clinical Research Progress on Deep Learning for Metal Artifact Reduction in Computed Tomography Images
摘要
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.关键词
CT成像/人工智能/深度学习/图像重建算法/金属伪影减少Key words
CT imaging/artificial intelligence/deep learning/image reconstruction algorithm/metal artifact reduction分类
信息技术与安全科学引用本文复制引用
刘相程,黄晓颖,魏鹏月,王鹏朝,郭方凯,暴云锋..深度学习去CT图像金属伪影的临床研究进展[J].CT理论与应用研究,2026,35(1):28-35,8.基金项目
河北省2019年度医学科学研究课题(20190005) （20190005）
河北省2023年度医学科学研究课题(20230409). （20230409）
