Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method. (30th June 2017) - Chen, Yuanyuan; Sha, Miao; Zhao, Xin; Ma, Jianguo; Ni, Hongyan; Gao, Wei; Ming, Dong
Abstract: 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. Graphical abstract: Highlights: A machine learning procedure that combines diffusivity and kurtosis is proposed. Detection with diffusion indices from DKI is more accurate than DTI at the dataset level. Kurtosis indices identify abnormalities that are supplemental to those observed with diffusivity indices. The combination of diffusivity and kurtosis performs better than either index alone.