胎儿大脑的解剖学约束纤维束成像
Anatomically Constrained Tractography of the Fetal Brain
AI总结：
针对胎儿dMRI数据质量低导致现有纤维束成像结果不准确的问题，提出基于dMRI空间脑组织自动分割的解剖约束纤维束成像方法，大幅提升成像准确性与胎儿脑定量评估的可靠性。
摘要：
扩散加权磁共振成像（dMRI）正越来越多地用于宫内胎儿大脑研究。dMRI支持的一项重要计算是流线纤维束成像，它具有脑白质纤维束特异性分析、结构连接性评估等独特应用。然而，由于胎儿dMRI数据质量较低，且纤维束成像本身具有挑战性，现有方法往往产生高度不准确的结果：它们生成大量错误流线，同时无法重建构成主要白质纤维束的流线。本文提出基于直接在dMRI空间中对胎儿脑组织进行准确分割的解剖学约束纤维束成像方法。我们开发了一种深度学习方法来自动计算分割结果。在独立测试数据上的实验表明，该方法能够准确分割胎儿脑组织，并大幅改善纤维束成像结果，能够重建视辐射等高度弯曲的纤维束。重要的是，我们的方法通过对dMRI数据进行扩散张量拟合，来推断组织分割结果和流线传播方向，因此适用于常规胎儿dMRI扫描。该方法可显著提升利用dMRI进行胎儿大脑定量评估的准确性和可重复性。
英文摘要：
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 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 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 lead to significant improvements in the accuracy and reproducibility of quantitative assessment of the fetal brain with dMRI.
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