Massachusetts General Hospital, Boston, United States.
Boston Children's Hospital, Boston, United States.
Neuroimage. 2019 Oct 1;199:1-17. doi: 10.1016/j.neuroimage.2019.05.051. Epub 2019 May 24.
The ongoing myelination of white-matter fiber bundles plays a significant role in brain development. However, reliable and consistent identification of these bundles from infant brain MRIs is often challenging due to inherently low diffusion anisotropy, as well as motion and other artifacts. In this paper we introduce a new tool for automated probabilistic tractography specifically designed for newborn infants. Our tool incorporates prior information about the anatomical neighborhood of white-matter pathways from a training data set. In our experiments, we evaluate this tool on data from both full-term and prematurely born infants and demonstrate that it can reconstruct known white-matter tracts in both groups robustly, even in the presence of differences between the training set and study subjects. Additionally, we evaluate it on a publicly available large data set of healthy term infants (UNC Early Brain Development Program). This paves the way for performing a host of sophisticated analyses in newborns that we have previously implemented for the adult brain, such as pointwise analysis along tracts and longitudinal analysis, in both health and disease.
正在进行的白质纤维束髓鞘形成在大脑发育中起着重要作用。然而,由于固有低扩散各向异性以及运动和其他伪影,从婴儿脑 MRI 中可靠且一致地识别这些束通常具有挑战性。在本文中,我们引入了一种新的工具,用于专门为新生儿进行自动概率追踪。我们的工具结合了来自训练数据集的关于白质通路解剖邻域的先验信息。在我们的实验中,我们在来自足月和早产儿的数据上评估了该工具,并证明即使在训练集和研究对象之间存在差异,它也可以在两组中稳健地重建已知的白质束。此外,我们还在一个公开的健康足月婴儿大型数据集(UNC 早期大脑发育计划)上对其进行了评估。这为在新生儿中进行一系列复杂的分析铺平了道路,我们之前已经在成人大脑中实现了这些分析,例如沿着束的点分析和纵向分析,无论是在健康还是疾病状态下。