Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

中文翻译：

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创建用于自动三叉神经识别的新型三叉神经束成像图集

扩散 MRI (dMRI) 纤维束成像已在许多临床和研究应用中成功用于研究三叉神经 (TGN)。目前，在纤维束成像数据中识别 TGN 需要专家使用手动绘制的感兴趣区域 (ROI) 进行神经选择，这很容易出现观察者之间的差异、耗时且带来较高的临床和劳动力成本。为了克服这些问题，我们建议创建一个新颖的解剖学策划的 TGN 纤维束成像图集，能够从 dMRI 纤维束成像中自动识别 TGN。在本文中，我们首先说明三叉神经纤维束成像图谱的创建。利用完善的计算流程和专业的神经解剖学知识，我们使用来自人类连接组项目 50 名受试者的纤维束描记数据生成数据驱动的 TGN 纤维聚类图谱。然后，我们演示了所提出的图集在新主题中自动 TGN 识别的应用，而不依赖于专家 ROI 放置。使用来自两个不同采集站点的 dMRI 数据进行定量和视觉实验，并与专家 TGN 识别进行比较。我们在空间重叠和可视化方面显示了自动识别的 TGN 和手动识别的 TGN 之间具有高度可比性的结果，而我们提出的方法有几个优点。首先，我们的方法执行自动 TGN 识别，因此它提供了一种有效的工具来减少专家人工成本和相对于专家手动选择的操作员间偏差。其次，我们的方法对潜在的成像伪影和/或噪声具有鲁棒性，这些伪影和/或噪声可能会阻止成功手动放置 ROI 以进行 TGN 选择，从而产生更高的成功 TGN 识别率。