Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method

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.

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

“…We may be able to further disentangle questions of orientation coherence (dispersing and 'kissing' fibers), fiber diameter, fiber density, membrane permeability, and myelination, which all influence classic anisotropy and diffusivity measures derived from DTI. Several AD studies have already used multi-shell protocols to compute diffusion indices from models that do not assume monoexponential decay, such as diffusion kurtosis imaging (DKI; Jensen et al, 2005;Chen et al, 2017;Cheng et al, 2018;, and multi-compartment models such as neurite orientation dispersion and density imaging (NODDI; Zhang et al, 2012;Colgan et al, 2016;Slattery et al, 2017;Parker et al, 2018). To date, approximately 20 participants in ADNI have been scanned with multishell diffusion protocols; in a future report, we will relate these measures to those examined here.…”

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

“…Table 4 shows some studies in Alzheimer's disease and other forms of dementia via machine learning algorithms. The applications include diagnosis of Alzheimer's disease [115,116], diagnosis of dementias [117], and detection of Alzheimer's disease related regions [118], prediction of mild cognitive impairment patients for conversion to Alzheimer's disease [119,120], detection of dissociable multivariate morphological patterns [121], diagnosis of both Alzheimer's disease and mild cognitive impairment [122] and identification of genes related to Alzheimer's disease [125,126]. Alzheimer's disease: sensitivity = 85%, specificity = 82%, accuracy = 85%; Mild cognitive impairment: sensitivity = 84%, specificity = 81%, accuracy = 85% [125] Identification of genes related to Alzheimer's disease DT; QAR 33 90 genes are related to Alzheimer's disease [126] Identification of genes related to Alzheimer's disease ELM; RF; SVM 31 Sensitivity= 78.77%; Specificity= 83.1%; Accuracy = 74.67% DCNN = deep convolutional neural network; DT = decision tree; ELM = extreme learning machine; EM = expectation maximization; GA = genetic algorithm; LC = lasso classification; LDS = low density separation; LR = logistic regression; NBC = Naive Bayes classifier; QAR = quantitative association rules; RF = random forest; RLO = random linear oracle; RS = random subspace; SVM = support vector machine.…”

“…Fourteen different algorithms were employed in [115][116][117][118][119][120][121][122][123][124][125][126]. The datasets of Alzheimer's disease and other forms of dementia have relatively small sample size.…”

“…Due to the multitude of smart healthcare applications, only four applications in the field of diseases diagnosis, cardiovascular diseases [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99], diabetes mellitus [100][101][102][103][104][105][106][107][108][109][110][111][112], Alzheimer's disease and other forms of dementias [113][114][115][116][117][118][119][120][121][122][123][124][125][126], and tuberculosis [127][128][129][130][13...…”

Correlations Between the Microstructural Changes of the Medial Temporal Cortex and Mild Cognitive Impairment in Patients With Cerebral Small Vascular Disease (cSVD): A Diffusion Kurtosis Imaging Study

“…However, the simplified description of the diffusion process assumed in DTI does not permit complex microstructures to be completely mapped because the cellular components and structures hinder and restrict the diffusion properties of water molecules. These limitations can be partially overcome by DKI, and DKI parameters have been found to be very sensitive in identifying some alterations that characterize many neurological diseases (11,12). These changes are appreciable with DKI even before any imaging findings through conventional imaging and in a better way than with conventional DTI (13).…”