Xie Lei, Huang Jiahao, Zhang Jiawei, He Jianzhong, Pan Yiang, Xie Guoqiang, Li Mengjun, Zeng Qingrun, Li Mingchu, Feng Yuanjing
IEEE Trans Biomed Eng. 2025 Aug 4;PP. doi: 10.1109/TBME.2025.3595182.
Cranial nerves (CNs) play a crucial role in various essential functions of the human brain, and mapping their pathways from diffusion MRI (dMRI) provides valuable preoperative insights into the spatial relationships between individual CNs and key tissues. However, mapping a comprehensive and detailed CN atlas is challenging because of the unique anatomical structures of each CN pair and the complexity of the skull base environment.
In this work, we present what we believe to be the first study to develop a comprehensive diffusion tractography atlas for automated mapping of CN pathways in the human brain. The CN atlas is generated by fiber clustering by using the streamlines generated by multi-parametric fiber tractography for each pair of CNs. Instead of disposable clustering, we explore a new strategy of multi-stage fiber clustering for multiple analysis of approximately 1,000,000 streamlines generated from the 50 subjects from the Human Connectome Project (HCP).
Quantitative and visual experiments demonstrate that our CN atlas achieves high spatial correspondence with expert manual annotations on multiple acquisition sites, including the HCP dataset, the Multi-shell Diffusion MRI (MDM) dataset and two clinical cases of pituitary adenoma patients.
The proposed CN atlas can automatically identify 8 fiber bundles associated with 5 pairs of CNs, including the optic nerve CN II, oculomotor nerve CN III, trigeminal nerve CN V and facial-vestibulocochlear nerve CN VII/VIII, and its robustness is demonstrated experimentally.
This work contributes to the field of diffusion imaging by facilitating more efficient and automated mapping the pathways of multiple pairs of CNs, thereby enhancing the analysis and understanding of complex brain structures through visualization of their spatial relationships with nearby anatomy.
颅神经在人类大脑的各种基本功能中起着至关重要的作用，通过扩散磁共振成像（dMRI）绘制其路径可为术前了解各颅神经与关键组织之间的空间关系提供有价值的见解。然而，由于每对颅神经独特的解剖结构以及颅底环境的复杂性，绘制全面而详细的颅神经图谱具有挑战性。
在这项工作中，我们展示了我们认为的第一项开发用于自动绘制人类大脑中颅神经路径的全面扩散张量成像图谱的研究。该颅神经图谱是通过对每对颅神经使用多参数纤维束成像生成的流线进行纤维聚类而生成的。我们探索了一种新的多阶段纤维聚类策略，而不是一次性聚类，用于对来自人类连接体项目（HCP）的50名受试者产生的约1,000,000条流线进行多次分析。
定量和可视化实验表明，我们的颅神经图谱在多个采集部位与专家手动标注具有高度的空间对应性，包括HCP数据集、多壳扩散磁共振成像（MDM）数据集以及两名垂体腺瘤患者的临床病例。
所提出的颅神经图谱可以自动识别与5对颅神经相关的8个纤维束，包括视神经CN II、动眼神经CN III、三叉神经CN V和面部 - 前庭蜗神经CN VII/VIII，并通过实验证明了其稳健性。
这项工作通过促进更高效和自动化地绘制多对颅神经的路径，为扩散成像领域做出了贡献，从而通过可视化它们与附近解剖结构的空间关系来增强对复杂脑结构的分析和理解。
