Parametric and non-parametric statistical analysis of DT-MRI data
Parametric and non-parametric statistical analysis of DT-MRI data
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DOI：
10.1016/s1090-7807(02)00178-7
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发表时间：
2003-03-01
影响因子：
2.2
通讯作者：
Basser, PJ
中科院分区：
文献类型：
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作者：
Pajevic, S;Basser, PJ
在这项工作中，提出了参数和非参数统计方法来分析扩散张量磁共振成像（DT-MRI）数据。提出了一种多元正态分布作为扩散张量数据的参数统计模型，当幅值MR图像不包含除约翰逊噪声以外的伪影时。我们测试这个模型使用蒙特卡罗（MC）模拟DT-MRI实验。这里提出的非参数方法是一种自举方法的实现，我们称之为DT-MRI自举。它被用来估计实验DT-MRI数据的经验概率分布，并对它们进行假设检验。DT-MRI引导程序还用于获得单个体素内和感兴趣区域（ROI）内的DT-MRI参数的各种统计数据;我们还使用引导程序来研究ROI中这些参数的固有变异性，独立于背景噪声。我们使用MC模拟评估DT-MRI引导程序，并将其应用于在体内人脑上采集的DT-MRI数据，以及具有均匀扩散特性的体模。出版社：Elsevier Science（USA）
In this work parametric and non-parametric statistical methods are proposed to analyze Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. A Multivariate Normal Distribution is proposed as a parametric statistical model of diffusion tensor data when magnitude MR images contain no artifacts other than Johnson noise. We test this model using Monte Carlo (MC) simulations of DT-MRI experiments. The non-parametric approach proposed here is an implementation of bootstrap methodology that we call the DT-MRI bootstrap. It is used to estimate an empirical probability distribution of experimental DT-MRI data, and to perform hypothesis tests on them. The DT-MRI bootstrap is also used to obtain various statistics of DT-MRI parameters within a single voxel, and within a region of interest (ROI); we also use the bootstrap to study the intrinsic variability of these parameters in the ROI, independent of background noise. We evaluate the DT-MRI bootstrap using MC simulations and apply it to DT-MRI data acquired on human brain in vivo, and on a phantom with uniform diffusion properties. Published by Elsevier Science (USA).