Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method
Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method
复制标题
使用扩散率和峰度组合方法自动检测阿尔茨海默病的病理性白质改变
DOI:
10.1016/j.pscychresns.2017.04.004
复制
发表时间:
2017-06-30
影响因子:
2.3
通讯作者:
Ming, Dong
中科院分区:
文献类型:
--
作者:
Chen, Yuanyuan;Sha, Miao;Ming, Dong
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in diseases such as Alzheimer's. The advantages of DKI over DTI have been reported generally; however, the indistinct relationship between diffusivity and kurtosis has not been clearly revealed in clinical settings. In this study, we hypothesize that the combination of diffusivity and kurtosis in DKI improves the capacity of DKI to detect Alzheimer's disease compared with diffusivity or kurtosis alone. Specifically, a support vector machine-based approach was applied to combine diffusivity and kurtosis and to compare different indices datasets. Strict assessments were conducted to ensure the reliability of all classifiers. Then, data from the optimized classifiers were used to detect abnormalities. With the combination, high accuracy performances of 96.23% were obtained in 53 subjects, including 27 Alzheimer's patients. More highly scored abnormal regions were selected by the combination than alone. The results revealed that more precise diffusivity and complementary kurtosis mainly contributed to the high performance of the combination in DKI. This study provides further understanding of DKI and the relationship between diffusivity and kurtosis in pathologic white matter alterations in Alzheimer's disease.