Purpose: Estimation of uncertainty of MAP-MRI metrics is an important topic, for several reasons. Bootstrap derived uncertainty, such as the standard deviation, provides valuable information, and can be incorporated in MAP-MRI studies to provide more extensive insight. Methods: In this paper, the uncertainty of different MAP-MRI metrics was quantified by estimating the empirical distributions using the wild bootstrap. We applied the wild bootstrap to both phantom data and human brain data, and obtain empirical distributions for the MAP-MRI metrics return-to-origin probability (RTOP), non-Gaussianity (NG), and propagator anisotropy (PA). Results: We demonstrated the impact of diffusion acquisition scheme (number of shells and number of measurements per shell) on the uncertainty of MAP-MRI metrics. We demonstrated how the uncertainty of these metrics can be used to improve group analyses, and to compare different preprocessing pipelines. We demonstrated that with uncertainty considered, the results for a group analysis can be different. Conclusion: Bootstrap derived uncertain measures provide additional information to the MAP-MRI derived metrics, and should be incorporated in ongoing and future MAP-MRI studies to provide more extensive insight.

中文翻译：

---

使用 Wild Bootstrap 量化平均表观传播子 MRI 的不确定性

目的：出于多种原因，估计 MAP-MRI 指标的不确定性是一个重要主题。Bootstrap 衍生的不确定性，如标准偏差，提供了有价值的信息，可以纳入 MAP-MRI 研究以提供更广泛的洞察力。方法：在本文中，通过使用野生引导程序估计经验分布来量化不同 MAP-MRI 指标的不确定性。我们将野生引导程序应用于体模数据和人脑数据，并获得 MAP-MRI 指标返回原点概率 (RTOP)、非高斯性 (NG) 和传播子各向异性 (PA) 的经验分布。结果：我们证明了扩散采集方案（壳数和每个壳的测量数）对 MAP-MRI 指标不确定性的影响。我们展示了如何使用这些指标的不确定性来改进组分析，并比较不同的预处理管道。我们证明，考虑到不确定性，组分析的结果可能会有所不同。结论：Bootstrap 衍生的不确定性度量为 MAP-MRI 衍生的度量提供了额外的信息，并且应该被纳入正在进行和未来的 MAP-MRI 研究中以提供更广泛的洞察力。