Yuki Kanazawa, Natsuki Ikemitsu, Yuki Kinjo, Masafumi Harada, Hiroaki Hayashi, Yo Taniguchi, Kosuke Ito, Yoshitaka Bito, Yuki Matsumoto, Akihiro Haga

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

  • General Medicine
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Abstract 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.

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