Automatic oculomotor nerve identification based on data‐driven fiber clustering

• Published in: Human brain mapping 2022-05, Vol.43 (7), p.2164-2180
• Main Authors: Huang, Jiahao, Li, Mengjun, Zeng, Qingrun, Xie, Lei, He, Jianzhong, Chen, Ge, Liang, Jiantao, Li, Mingchu, Feng, Yuanjing
• Format: Article
• Language: English
• Subjects: Automation; Brain cancer; Brain stem; Cluster Analysis; Clustering; Computer applications; data‐driven; diffusion magnetic resonance imaging; Diffusion Magnetic Resonance Imaging - methods; Diffusion Tensor Imaging - methods; fiber clustering; Fiber orientation; Fibers; Humans; Identification; Image processing; Image Processing, Computer-Assisted - methods; Labor costs; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Methods; Muscles; neurosurgery; Oculomotor nerve; Oculomotor Nerve - diagnostic imaging; Oculomotor nerves; Patients; Spherical shells; tractography; Tumors
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.25779

The oculomotor nerve (OCN) is the main motor nerve innervating eye muscles and can be involved in multiple flammatory, compressive, or pathologies. The diffusion magnetic resonance imaging (dMRI) tractography is now widely used to describe the trajectory of the OCN. However, the complex cranial structure leads to difficulties in fiber orientation distribution (FOD) modeling, fiber tracking, and region of interest (ROI) selection. Currently, the identification of OCN relies on expert manual operation, resulting in challenges, such as the carries high clinical, time‐consuming, and labor costs. Thus, we propose a method that can automatically identify OCN from dMRI tractography. First, we choose the multi‐shell multi‐tissue constraint spherical deconvolution (MSMT‐CSD) FOD estimation model and deterministic tractography to describe the 3D trajectory of the OCN. Then, we rely on the well‐established computational pipeline and anatomical expertise to create a data‐driven OCN tractography atlas from 40 HCP data. We identify six clusters belonging to the OCN from the atlas, including the structures of three kinds of positional relationships (pass between, pass through, and go around) with the red nuclei and two kinds of positional relationships with medial longitudinal fasciculus. Finally, we apply the proposed OCN atlas to identify the OCN automatically from 40 new HCP subjects and two patients with brainstem cavernous malformation. In terms of spatial overlap and visualization, experiment results show that the automatically and manually identified OCN fibers are consistent. Our proposed OCN atlas provides an effective tool for identifying OCN by avoiding the traditional selection strategy of ROIs. In this work, we propose an automatic oculomotor nerve (OCN) identification method. We choose the multi‐shell multi‐tissue constraint spherical deconvolution (MSMT‐CSD) FOD estimation model and deterministic tractography to describe the three dimensional trajectory of the OCN after investigation the performance of different tractography methods for the reconstruction of the complete OCN pathway. Then, we rely on the well‐established computational pipeline and anatomical expertise to create a data‐driven OCN fiber clustering atlas from 40 HCP data. We identify six clusters belonging to the OCN from the atlas, including the structures of three kinds of positional relationships (pass between, pass through, and go around) with the red nuclei and two kinds of positional re