Baby 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.
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom.
Neuroimage. 2023 Dec 1;283:120397. doi: 10.1016/j.neuroimage.2023.120397. Epub 2023 Oct 18.
Diffusion-weighted MRI (dMRI) is a medical imaging method that can be used to investigate the brain microstructure and structural connections between different brain regions. The method, however, requires relatively complex data processing frameworks and analysis pipelines. Many of these approaches are vulnerable to signal dropout artefacts that can originate from subjects moving their head during the scan. To combat these artefacts and eliminate such outliers, researchers have proposed two approaches: to replace outliers or to downweight outliers during modelling and analysis. With the rising interest in dMRI for clinical research, these types of corrections are increasingly important. Therefore, we set out to investigate the differences between outlier replacement and weighting approaches to help the dMRI community to select the best tool for their data processing pipelines. We evaluated dMRI motion correction registration and single tensor model fit pipelines using Gaussian Process and Spherical Harmonic based replacement approaches and outlier downweighting using highly realistic whole-brain simulations. As a proof of concept, we applied these approaches to dMRI infant data sets that contained varying numbers of dropout artefacts. Based on our results, we concluded that the Gaussian Process based outlier replacement provided similar tensor fit results to Gaussian Process based outlier detection and downweighting. Therefore, if only the least-squares estimate of the single tensor model is of interest, our recommendation is to use outlier replacement. However, outlier downweighting can potentially provide a more accurate estimate of the model precision which could be relevant for applications such as probabilistic tractoraphy.
扩散加权磁共振成像（dMRI）是一种医学成像方法，可用于研究大脑的微观结构和不同大脑区域之间的结构连接。然而，该方法需要相对复杂的数据处理框架和分析管道。其中许多方法容易受到信号缺失伪影的影响，这些伪影可能源于被试在扫描过程中头部移动。为了应对这些伪影并消除这些异常值，研究人员提出了两种方法：在建模和分析过程中替换异常值或降低异常值的权重。随着 dMRI 在临床研究中的兴起，这些类型的校正变得越来越重要。因此，我们着手研究异常值替换和加权方法之间的差异，以帮助 dMRI 社区为其数据处理管道选择最佳工具。我们使用基于高斯过程和球谐函数的替换方法以及基于高度逼真的全脑模拟的异常值降权方法，评估了 dMRI 运动校正配准和单张量模型拟合管道。作为概念验证，我们将这些方法应用于包含不同数量的缺失伪影的 dMRI 婴儿数据集。根据我们的结果，我们得出结论，基于高斯过程的异常值替换提供了与基于高斯过程的异常值检测和降权相似的张量拟合结果。因此，如果只对单张量模型的最小二乘估计感兴趣，我们建议使用异常值替换。然而，异常值降权可能会提供更准确的模型精度估计，这对于概率追踪等应用可能很重要。
