Differences of white matter structure for diffusion kurtosis imaging using voxel-based morphometry and connectivity analysis.

Objectives: In a clinical study, diffusion kurtosis imaging (DKI) has been used to visualize and distinguish white matter (WM) structures' details. The purpose of our study is to evaluate and compare the diffusion tensor imaging (DTI) and DKI parameter values to obtain WM structure differences of healthy subjects. Methods: Thirteen healthy volunteers (mean age, 25.2 years) were examined in this study. On a 3-T MRI system, diffusion dataset for DKI was acquired using an echo-planner imaging sequence, and T1-weghted (T1w) images were acquired. Imaging analysis was performed using Functional MRI of the brain Software Library (FSL). First, registration analysis was performed using the T1w of each subject to MNI152. Second, DTI (eg, fractional anisotropy [FA] and each diffusivity) and DKI (eg, mean kurtosis [MK], radial kurtosis [RK], and axial kurtosis [AK]) datasets were applied to above computed spline coefficients and affine matrices. Each DTI and DKI parameter value for WM areas was compared. Finally, tract-based spatial statistics (TBSS) analysis was performed using each parameter. Results: The relationship between FA and kurtosis parameters (MK, RK, and AK) for WM areas had a strong positive correlation (FA-MK, R2 = 0.93; FA-RK, R2 = 0.89) and a strong negative correlation (FA-AK, R2 = 0.92). When comparing a TBSS connection, we found that this could be observed more clearly in MK than in RK and FA. Conclusions: WM analysis with DKI enable us to obtain more detailed information for connectivity between nerve structures. Advances in knowledge: Quantitative indices of neurological diseases were determined using segmenting WM regions using voxel-based morphometry processing of DKI images.

Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation.

BACKGROUND: To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonance imaging. Detailed morphological changes in a specific tissue area having the most evidence of atrophy were not considered by the machine-learning technique. PURPOSE: To develop a machine-learning system that can capture morphology features for determination of atrophy of brain tissue in early-stage AD and classification of healthy participants or patients. MATERIAL AND METHODS: Three-dimensional T1-weighted (3D-T1W) data were obtained from AD Neuroimaging Initiative (200 healthy controls and 200 patients with early-stage AD). Automated segmentation of 3D-T1W data was performed. Deep learning (DL) and support vector machine (SVM) were trained using 66-segmented volume values as input and AD diagnosis as output. DL was performed using 66 volume values or gray matter (GM) and white matter (WM) volume values. SVM learning was performed using 66 volume values and six regions with high variable importance. 3D convolutional neural network (3D-CNN) was trained using the segmented images. Accuracy and area under curve (AUC) were obtained. Variable importance was evaluated from logistic regression analysis. RESULTS: DL for GM and WM volume values, accuracy 0.6; SVM for all volume values, accuracy 0.82 and AUC 0.81; DL for all volume values, accuracy 0.82 and AUC 0.8; 3D-CNN using segmental images of the whole brain, accuracy 0.5 and AUC 0.51. SVM using volume values of six regions, accuracy 0.82; image-based 3D-CNN, highest accuracy 0.69. CONCLUSION: Our results show that atrophic features are more considerable than morphological features in the early detection of AD.

Development of Correction for Signal-to-Noise Ratio Using a T2* With Improved Phase Method.

OBJECTIVE: This study aims to improve the signal-to-noise ratio (SNR) of the phase image focusing T2 and to develop an improved phase (iPhase) image acquired high SNR. METHODS: The iPhase images of phantom and brain were acquired with multi-echo spoiled gradient-echo. The phantom component was a gadopentetate dimeglumine (Gd-DTPA) solution made of different concentrations (0.1, 0.5, and 1 wt%) and Gd-DTPA (0.1, 0.5, and 1 wt%) with agar (1.0 wt%). We applied the iPhase image to susceptibility weighed image (SWI) and evaluated SNR of SWI. RESULTS: In phantom study, SNRs of conventional SWI at each sample were 19.8, 15.7, 7.4, 20.0, 17.4, and 27.3, respectively. Signal-to-noise ratios of SWI derived from iPhase method were 29.5, 33.7, 21.7, 28.5, 24.3, and 14.7, respectively. Then, the SNR showed an improvement of 196% at maximum (for the Gd-DTPA 1 wt% sample). In healthy volunteer study, SWI derived from iPhase method had the good contrast between white matter and gray matter. CONCLUSIONS: The iPhase image was able to improve the phase SNR. Moreover, iPhase method makes it possible to obtain a high SNR image when applying to SWI.