Whitcher Brandon, Tuch David S, Wisco Jonathan J, Sorensen A Gregory, Wang Liqun
Clinical Imaging Centre, GlaxoSmithKline, Hammersmith Hospital, London, UK.
Hum Brain Mapp. 2008 Mar;29(3):346-62. doi: 10.1002/hbm.20395.
Estimation of noise-induced variability in diffusion tensor imaging (DTI) is needed to objectively follow disease progression in therapeutic monitoring and to provide consistent readouts of pathophysiology. The noise variability of nonlinear quantities of the diffusion tensor (e.g., fractional anisotropy, fiber orientation, etc.) have been quantified using the bootstrap, in which the data are resampled from the experimental averages, yet this approach is only applicable to DTI scans that contain multiple averages from the same sampling direction. It has been shown that DTI acquisitions with a modest to large number of directions, in which each direction is only sampled once, outperform the multiple averages approach. These acquisitions resist the traditional (regular) bootstrap analysis though. In contrast to the regular bootstrap, the wild bootstrap method can be applied to such protocols in which there is only one observation per direction. Here, we compare and contrast the wild bootstrap with the regular bootstrap using Monte Carlo numerical simulations for a number of diffusion scenarios. The regular and wild bootstrap methods are applied to human DTI data and empirical distributions are obtained for fractional anisotropy and the diffusion tensor eigensystem. Spatial maps of the estimated variability in the diffusion tensor principal eigenvector are provided. The wild bootstrap method can provide empirical distributions for tensor-derived quantities, such as fractional anisotropy and principal eigenvector direction, even when the exact distributions are not easily derived.
在治疗监测中客观跟踪疾病进展并提供一致的病理生理学读数，需要估计扩散张量成像（DTI）中噪声引起的变异性。扩散张量的非线性量（例如，分数各向异性、纤维取向等）的噪声变异性已通过自展法进行量化，其中数据是从实验平均值中重新采样得到的，但这种方法仅适用于包含来自相同采样方向的多个平均值的DTI扫描。研究表明，具有适度到大量方向的DTI采集（其中每个方向仅采样一次）优于多个平均值方法。然而，这些采集无法进行传统的（常规）自展分析。与常规自展法不同，野生自展法可应用于每个方向只有一个观测值的此类协议。在此，我们使用蒙特卡罗数值模拟针对多种扩散场景将野生自展法与常规自展法进行比较和对比。将常规和野生自展法应用于人体DTI数据，并获得分数各向异性和扩散张量本征系统的经验分布。提供了扩散张量主本征向量中估计变异性的空间图。即使在难以得出精确分布的情况下，野生自展法也可为张量衍生量（如分数各向异性和主本征向量方向）提供经验分布。
