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Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification


Zhang, Fan; Xie, Guoqiang; Leung, Laura; Mooney, Michael A; Epprecht, Lorenz; Norton, Isaiah; Rathi, Yogesh; Kikinis, Ron; Al-Mefty, Ossama; Makris, Nikos; Golby, Alexandra J; O’Donnell, Lauren J (2020). Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification. NeuroImage, 220:117063.

Abstract

Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

Abstract

Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Otorhinolaryngology
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > Neurology
Life Sciences > Cognitive Neuroscience
Uncontrolled Keywords:Cognitive Neuroscience, Neurology
Language:English
Date:1 October 2020
Deposited On:26 Jan 2021 14:43
Last Modified:24 Apr 2024 01:54
Publisher:Elsevier
ISSN:1053-8119
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.neuroimage.2020.117063
PubMed ID:32574805
  • Content: Published Version
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)