■在一项临床研究中,扩散峰度成像(DKI)已用于可视化和区分白质(WM)结构的细节。我们研究的目的是评估和比较扩散张量成像(DTI)和DKI参数值,以获得健康受试者的WM结构差异。
■13名健康志愿者(平均年龄,25.2年)在这项研究中进行了检查。在3-T核磁共振系统上,DKI的扩散数据集是使用回波规划成像序列采集的,并采集T1-weighted(T1w)图像。使用脑软件库(FSL)的功能性MRI进行成像分析。首先,使用每位受试者的T1w对MNI152进行登记分析.第二,DTI(例如,分数各向异性[FA]和每个扩散率)和DKI(例如,平均峰度[MK],径向峰度[RK],和轴向峰度[AK])数据集被应用于上面计算的样条系数和仿射矩阵。比较WM区域的每个DTI和DKI参数值。最后,使用每个参数进行基于道的空间统计(TBSS)分析。
■FA与峰度参数之间的关系(MK,RK,和AK)对于WM区域具有很强的正相关(FA-MK,R2=0.93;FA-RK,R2=0.89)和强负相关(FA-AK,R2=0.92)。比较TBSS连接时,我们发现在MK中比在RK和FA中更清楚地观察到这一点。
■带有DKI的WM分析使我们能够获得有关神经结构之间连通性的更多详细信息。
■使用DKI图像的基于体素的形态计量学处理,使用分割WM区域来确定神经疾病的定量指数。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: WM analysis with DKI enable us to obtain more detailed information for connectivity between nerve structures.
UNASSIGNED: Quantitative indices of neurological diseases were determined using segmenting WM regions using voxel-based morphometry processing of DKI images.