Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging that accounts for leading non-Gaussian diffusion effects. In DKI studies, a wide range of different gradient strengths (b-values) is used, which is known to affect the estimated diffusivity and kurtosis parameters. Hence there is a need to assess the accuracy and precision of the estimated parameters as a function of b-value. This work examines the error in the estimation of mean of the kurtosis tensor (MKT) with respect to the ground truth, using simulations based on a biophysical model for both gray (GM) and white (WM) matter. Model parameters are derived from densely sampled experimental data acquired in ex vivo rat brain and in vivo human brain. Additionally, the variability of MKT is studied using the experimental data. Prevalent fitting protocols are implemented and investigated. The results show strong dependence on the maximum b-value of both net relative error and standard deviation of error for all of the employed fitting protocols. The choice of b-values with minimum MKT estimation error and standard deviation of error was found to depend on the protocol type and the tissue. Protocols that utilize two terms of the cumulant expansion (DKI) were found to achieve minimum error in GM at b-values less than 1 ms/μm2, whereas maximal b-values of about 2.5 ms/μm2 were found to be optimal in WM. Protocols including additional higher order terms of the cumulant expansion were found to provide higher accuracy for the more commonly used b-value regime in GM, but were associated with higher error in WM. Averaged over multiple voxels, a net average error of around 15% for both WM and GM was observed for the optimal b-value choice. These results suggest caution when using DKI generated metrics for microstructural modeling and when comparing results obtained using different fitting techniques and b-values.
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机译:扩散峰度成像(DKI)是扩散张量成像的扩展,它解释了主要的非高斯扩散效应。在DKI研究中,使用了各种不同的梯度强度(b值),已知这会影响估计的扩散率和峰度参数。因此,有必要根据b值评估估计参数的准确性和精确度。这项工作使用基于生物物理模型的灰色(GM)和白色(WM)物质的模拟,检验了相对于地面真实性的峰度张量(MKT)平均值估计中的误差。模型参数来自在离体大鼠脑和体内人脑中获得的密集采样的实验数据。此外,使用实验数据研究了MKT的变异性。普遍的拟合协议已实施和调查。结果表明,对于所有采用的拟合方案,净相对误差和误差的标准偏差的最大b值都具有很强的依赖性。发现具有最小MKT估计误差和标准误差的b值的选择取决于方案类型和组织。发现使用两项累积量扩展(DKI)的协议在b值小于1 ms /μm 2 sup>的情况下在GM中实现了最小误差,而最大b值约为2.5 ms /μm发现 2 sup>在WM中是最佳的。已发现包括累积量扩展的其他更高阶项的协议可为GM中更常用的b值方案提供更高的准确性,但与WM中的更高错误相关。对于多个体素进行平均,对于最佳b值选择,WM和GM的净平均误差均约为15%。当使用DKI生成的度量进行微观结构建模以及比较使用不同拟合技术和b值获得的结果时,这些结果建议谨慎。
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