首页 | 本学科首页   官方微博 | 高级检索  
     

功能性磁共振成像在轻度认知障碍检测诊断的研究综述
引用本文:安兴伟,周宇涛,狄洋,刘爽,明东. 功能性磁共振成像在轻度认知障碍检测诊断的研究综述[J]. 中国生物医学工程学报, 2022, 41(1): 100-107. DOI: 10.3969/j.issn.0258-8021.2022.01.011
作者姓名:安兴伟  周宇涛  狄洋  刘爽  明东
作者单位:1(天津大学医学工程与转化医学研究院,天津 300072)2(天津市脑科学中心,天津 300072)3(天津大学精密仪器与光电子工程学院,天津 300072)
基金项目:国家重点研发计划项目(2017YFB1300302);;国家自然科学基金(61603269,81630051);
摘    要:现代社会中,阿尔茨海默病已经成为严重影响和限制个人日常生活甚至危及患者生命安全的一种疾病.轻度认知障碍作为阿尔茨海默病的前一个阶段,对其精确诊断有助于干预或降低患者转化为阿尔茨海默病的几率.目前,功能磁共振成像技术已经广泛应用于轻度认知障碍的检测诊断研究中.从特征提取、特征选择、数据降维和分类识别等方面,对fMRI在M...

关 键 词:轻度认知障碍  阿尔茨海默病  功能磁共振成像  机器学习  分类识别
收稿时间:2020-07-27

Review of Functional Magnetic Resonance Imagingin Diagnosis of Mild Cognitive Impairment
An Xingwei,Zhou Yutao,Di Yang,Liu Shuang,Ming Dong. Review of Functional Magnetic Resonance Imagingin Diagnosis of Mild Cognitive Impairment[J]. Chinese Journal of Biomedical Engineering, 2022, 41(1): 100-107. DOI: 10.3969/j.issn.0258-8021.2022.01.011
Authors:An Xingwei  Zhou Yutao  Di Yang  Liu Shuang  Ming Dong
Affiliation:(Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China)(Tianjin Center for Brain Science, Tianjin 300072, China)(Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China)
Abstract:Nowadays Alzheimer's disease (AD) has severely influenced and limited personal daily life and even posed a grave threat to the life and health of patients. Mild cognitive impairment (MCI) is the prodromal stage of AD, and accurate diagnosis can help to interfere or reduce the conversion of patients to Alzheimer's disease. At present, functional magnetic resonance imaging (fMRI) technology have been widely used in the detection and diagnosis of MCI. This article introduced the research status of fMRI in MCI from the aspects of feature extraction, feature selection, data dimensionality reduction and classification recognition. First, the commonly used resolution indicators such as low-frequency amplitude, local consistency, and functional connection for feature extraction was introduced. Second, features selection and data dimension reduction methods were introduced, and the efficient machine learning and deep learning algorithms in classification and recognition were summarized. This paper also proposed the remained problems and made perspectives to the future research.
Keywords:mild cognitive impairment  Alzheimer's disease  functional magnetic resonance imaging(fMRI)  machine learning  classification  
点击此处可从《中国生物医学工程学报》浏览原始摘要信息
点击此处可从《中国生物医学工程学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号