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Automatic oculomotor nerve identification based on data-driven fiber clustering.

Jiahao Huang^1,2, Mengjun Li^3,4, Qingrun Zeng^1,2

¹Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.

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|January 29, 2022

Summary

This summary is machine-generated.

This study introduces an automated method for identifying the oculomotor nerve (OCN) using diffusion MRI tractography. The developed OCN atlas enables accurate and efficient OCN identification, overcoming manual limitations.

Keywords:

data-driven diffusion magnetic resonance imaging fiber clustering neurosurgery oculomotor nerve tractography

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Area of Science:

Neuroimaging
Neuroanatomy

Background:

The oculomotor nerve (OCN) is crucial for eye movement but challenging to visualize with diffusion MRI tractography due to complex cranial anatomy.
Current manual identification of OCN is time-consuming, costly, and prone to inter-operator variability.

Purpose of the Study:

To develop an automated method for OCN identification from diffusion MRI tractography.
To create a data-driven OCN tractography atlas to aid in automated identification.

Main Methods:

Utilized multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) for fiber orientation distribution estimation.
Employed deterministic tractography and an atlas derived from 40 HCP datasets.
Defined OCN clusters based on relationships with red nuclei and medial longitudinal fasciculus.

Main Results:

An automated OCN identification method was successfully developed and validated.
The automated method demonstrated high consistency with manual identification in terms of spatial overlap and visualization.
The OCN atlas effectively identified OCN in new subjects and patients with brainstem cavernous malformation.

Conclusions:

The proposed OCN atlas provides an effective, automated tool for OCN identification from diffusion MRI tractography.
This method overcomes the limitations of traditional manual ROI selection, offering improved efficiency and accuracy.
The automated approach has potential applications in clinical diagnosis and research involving OCN pathologies.