PDF(Pubmed)
Abstract：
Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and the challenging nature of tractography, existing methods tend to produce highly inaccurate results. They generate many false streamlines while failing to reconstruct the streamlines that constitute the major white matter tracts. In this paper, we advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. We develop a deep learning method to compute the segmentation automatically. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve the tractography results. It enables the reconstruction of highly curved tracts such as optic radiations. Importantly, our method infers the tissue segmentation and streamline propagation direction from a diffusion tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans. The proposed method can facilitate the study of fetal brain white matter tracts with dMRI.
摘要：
弥散加权磁共振成像（dMRI）越来越多地用于研究子宫内的胎儿大脑。dMRI实现的一个重要计算是流线型纤维束成像，它具有独特的应用，例如脑白质的道特异性分析和结构连通性评估。然而,由于胎儿dMRI数据质量低和纤维束造影的挑战性，现有的方法往往会产生高度不准确的结果。它们产生许多错误的流线，而无法重建构成主要白质束的流线。在本文中,我们主张基于直接在dMRI空间中准确分割胎儿脑组织的解剖学约束纤维束成像。我们开发了一种深度学习方法来自动计算分割。在独立测试数据上的实验表明，该方法可以准确地分割胎儿脑组织，并大大提高了纤维束造影结果。它能够重建高度弯曲的束，如光辐射。重要的是,我们的方法从扩散张量拟合dMRI数据推断组织分割和流线传播方向，使其适用于常规胎儿dMRI扫描。所提出的方法可以促进dMRI对胎儿脑白质束的研究。
