arXiv 2507.232452025-07-31cs.CV

颅神经II、III、V及VII/VIII通路的自动映射：多参数多阶段扩散纤维束图谱

Automated Mapping the Pathways of Cranial Nerve II, III, V, and VII/VIII: A Multi-Parametric Multi-Stage Diffusion Tractography Atlas

Advanced Interdisciplinary Science and Technology, Zhejiang University of Technology（浙江工业大学先进交叉学科技术学院）；College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；Department of Radiology, Xiangya Hospital, Central South University（中南大学湘雅医院放射科）；Department of Neurosurgery, Capital Medical University Xuanwu Hospital（首都医科大学北京协和医院神经外科）；Department of Neurosurgery, Nuclear Industry 215 Hospital of Shaanxi Province（陕西核工业215医院神经外科）

Lei Xie, Jiahao Huang, Jiawei Zhang, Jianzhong He, Yiang Pan, Guoqiang Xie, Mengjun Li, Qingrun Zeng, Mingchu Li, Yuanjing Feng

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AI总结：

本研究针对颅神经通路绘制难题，提出基于多参数多阶段纤维聚类的颅神经扩散图谱，可自动识别5对颅神经的8条纤维束，在多数据集上验证了高精度与鲁棒性，助力术前脑结构分析。

摘要：

颅神经（CNs）在人脑的多种核心功能中发挥着关键作用，通过扩散磁共振成像（dMRI）绘制其通路可为术前了解各颅神经与关键组织的空间关系提供重要参考。然而，由于每对颅神经的解剖结构独特，且颅底环境复杂，构建全面详尽的颅神经图谱极具挑战性。\n在本研究中，我们提出了首个用于自动映射人脑颅神经通路的综合扩散纤维束图谱。该颅神经图谱通过纤维聚类生成，聚类所用的流线来自针对每对颅神经的多参数纤维束成像。我们未采用一次性聚类，而是探索了多阶段纤维聚类新策略，用于对人类连接组计划（HCP）的50名受试者生成的约100万条流线进行多重分析。\n定量与可视化实验表明，我们的颅神经图谱在多个采集站点的数据上均与专家手动标注具有高度空间一致性，涉及的数据集包括HCP数据集、多壳扩散MRI（MDM）数据集以及两例垂体腺瘤患者的临床病例。该图谱可自动识别与5对颅神经相关的8条纤维束，包括视神经CN II、动眼神经CN III、三叉神经CN V以及面-前庭蜗神经CN VII/VIII，实验验证了其鲁棒性。本研究通过推动多对颅神经通路更高效的自动绘制，为扩散成像领域做出贡献，进而通过可视化颅神经与邻近解剖结构的空间关系，深化对复杂脑结构的分析与理解。

英文摘要：

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.

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