扩散权重MRI中的异常值:探索检测模型和缓解策略
Viljami Sairanen1, Jesper Andersson2
1Baby Brain Activity Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Department of Radiology, Kanta-Häme Central Hospital, Hämeenlinna, Finland.
NeuroImage
|October 11, 2023
概括
扩散权重MRI (dMRI) 处理从异常值校正中受益. 高斯过程异常值的替换提供了类似的张量适合结果减重,使其成为单张量模型估计的理想选择.
科学领域:
- 神经成像是一种神经成像.
- 医学物理 医学物理
- 生物医学工程 生物医学工程
背景情况:
- 扩散权重MRI (dMRI) 对于研究大脑微观结构和连接性至关重要.
- dMRI 数据处理是复杂的,容易受到运动诱导的信号失效.
- 准确的文物校正对于临床dMRI研究至关重要.
研究的目的:
- 为了比较异常替代和减权方法用于dMRI数据处理.
- 引导dMRI社区选择最佳的数据处理工具.
- 评估这些方法对运动校正和张量模型的影响.
主要方法:
- 模拟了现实的全脑dMRI数据,具有不同的脱落文物.
- 基于应用高斯过程 (GP) 和球体波 (SH) 的异常值替换.
- 实施的异常值下权衡技术.
- 评估了运动校正,注册和单张量模型的合适性.
主要成果:
- 基于GP的异常值替换产生了与基于GP的异常值下权衡可比的张量适应结果.
- 这两种方法都有效地解决了模拟和婴儿dMRI数据中的信号丢失工件.
- 偏差值下加权可能提供更好的模型精度估计.
结论:
- 当主要关注的是单张量模型的最小平方估计时,建议替换异常值.
- 偏差值下加权可能更适合需要精确模型估计的应用,例如概率学曲谱学.
- 方法之间的选择取决于dMRI研究中的具体分析目标.
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