2.1. Data acquisition and preprocessing

Ninety HCP subjects are used in this work, 10 of which are to compare multiple tractography methods and parameters, 40 to generate OCN atlas, and the remaining 40 to validate the proposed method. In addition, we apply the proposed method to two BSCM patients with brainstem tumors. The follows are the detailed parameters and preprocessing:

The HCP provides high‐quality dMRI and T1‐weighted (T1w) data, which are approved by the local Institutional Review Board of Washington University. The dMRI acquisition parameters in HCP are as follows: TR = 5,520 ms; TE = 89.5 ms; FA = 78°; voxel size = 1.25 × 1.25 × 1.25 mm³; FOV = 210 × 180 mm²; b‐values = 1,000, 2,000, and 3,000 s/mm²; and 90, 90, and 90 diffusion sampling directions. The T1w acquisition parameters in HCP are as follows: TR = 2,400 ms; TE = 2.14 ms; and voxel size = 0.7 × 0.7 × 0.7 mm³. Detailed information about the HCP data acquisition and preprocessing can be found in http://www.humanconnectomeproject.org/ (Glasser et al., 2013).

The MRI data of the patient with tumor are acquired at Xuanwu Hospital Capital Medical University by using the Siemens Skyra 3T scanner. dMRI acquisition parameters in tumor patient data are: TR = 8,900 ms, TE = 95 ms, b‐values = 1,000 s/mm², 60 diffusion sampling directions, and voxel size = 2.2 × 2.2 × 2.2 mm³. The T1w image acquisition parameters in the data of patient with tumor are as follows: TR = 2,400 ms; TE = 2.27 ms; 192 slices; FOV = 250 mm²; and voxel size = 1.0 × 1.0 × 1.0 mm³. Written informed consent forms are signed by all subjects, and the ethics committee at Xuanwu Hospital, Capital Medical University has given its permission before testing.

We design a series of processing pipelines, including motion correction, denoising, and eddy current correction. First, we apply denoising and eddy correction on dMRI and denoising on T1w images (Tournier et al., 2019).

Second, the automated reconstruction and labeling of cortical and subcortical regions are performed using the Freesurfer (Fischl, 2012) on T1w images. The seed imaging and regions of avoidance (ROAs) are chosen by automatically extracting the corresponding numbered brain area. We set the whole brainstem as the seed imaging (as shown in Figure 2). In terms of anatomical considerations, the anatomical pathway of the OCN originates from the ONC which is located in the central and dorsal midbrain (Brazis et al., 2012; Condos, 2021). Then OCN crosses the ventral midbrain and enters the cistern from the outer edge of the interpeduncular fossa (Park et al., 2017). And the rest of the brain regions (i.e., the whole brain excepts brainstem) are used as (ROAs. Because the pathway of the cisternal segment of OCN does not enter any other brain areas (Park et al., 2017).

Oculomotor nerve (OCN) atlas generation. (a) OCN tractography performs under the constraints of region of avoidance (ROA) and seed imaging. (b) Registration of 40 subject brainstem fibers to a common atlas space. (c) Spectral clustering for the generation of a fiber clustering atlas by using the brainstem fibers from 40 atlas subjects. (d,e) Manual selection of OCN subjects through initial cluster selection on the basis of expert anatomical experience. Six clusters, including OCN (four clusters) and OCNM (two clusters), are identified to belong to OCN on the basis of expert neuroanatomical knowledge