Optimizing Diffusion Tensor Imaging Tractography of the Trigeminal Nerve in the Brainstem
Poster No:
1922
Submission Type:
Abstract Submission
Authors:
Akshit Ayri1, Tejas Sankar1
Institutions:
1University of Alberta, Edmonton, Alberta
First Author:
Co-Author:
Introduction:
Trigeminal Neuralgia (TN) is a debilitating condition characterized by severe, unilateral facial pain, often associated with neurovascular compression at the root entry zone (REZ) of the trigeminal nerve (CNV) (Lambru et al., 2021). While structural MRI can identify compression at the REZ, and diffusion tensor imaging (DTI) can reveal CNV microstructural changes along its cisternal length, it remains challenging to delineate fibers from CNV as they enter into the brainstem and synapse in the spinal trigeminal nucleus (SN), which is an important relay station for facial pain information. Accurate visualization of these fibers which make up the spinal trigeminal tract (STr) with conventional DTI faces challenges such as low resolution, crossing fibers, and partial volume effects (Neetu et al., 2016; Li et al., 2017). This study aimed to systematically compare different DTI acquisition protocols and tractography algorithms to identify an optimal method for accurately visualizing the STr tract.
Methods:
We acquired DTI data from eight healthy controls (HCs) using three protocols: whole-brain isotropic (WBi) with a voxel resolution of 2 mm³, whole-brain anisotropic (WBa) with a voxel resolution of 1.9 x 1.9 x 3 mm³, and high-resolution FLAIR-DTI (voxel resolution 0.6 x 0.6 x 1.2 mm³). A subset of participants underwent multi-shell FLAIR-DTI (b=1000/2000 s/mm², with identical voxel resolution as FLAIR-DTI). Deterministic (ExploreDTI, Leemans et al., 2009) and probabilistic tractography (FSL, Behrens et al., 2007) were applied for reconstructing the STr, with regions of interest (ROIs) placed at the CNV cisternal segment and an atlas-based location of the SN (Figure 1). A blinded, standardized rating system assessed tract outputs for true positives (anatomically accurate STr) and avoidance of false positives (spurious fibers entering neighboring regions such as cerebellar peduncles or temporal lobes). The optimal protocol based on these ratings was then validated in three TN patients to confirm clinical feasibility.
Results:
Results showed a clear advantage of probabilistic tractography over deterministic methods. A deterministic algorithm failed to reconstruct STr tracts in WBi and WBa acquisitions. While deterministic tractography showed improved performance using high-resolution FLAIR-DTI, generating STr tracts in 56.25% of cases, false positives were still observed. In contrast, probabilistic tractography successfully delineated STr tracts across all acquisitions, achieving tract generation rates of 68.75% (WBi), 87.5% (WBa), and 100% with FLAIR-DTI. Importantly, high-resolution FLAIR-DTI outperformed other protocols, avoiding false positives in 56.25% of all STr tracts. Multi-shell FLAIR-DTI (b=1000/2000) produced SN tracts in all cases; however, it avoided spurious tracking in only 37.5%, suggesting limited additional benefit over single-shell FLAIR-DTI. Validation in TN patients was successful, with SN tracts successfully visualized using FLAIR-DTI and probabilistic tracking, meeting true positive criteria and avoiding spurious fibers (Figure 2). Inter-rater reliability showed strong agreement for STr tract presence (100%) and moderate agreement for false positive avoidance (88%).
Conclusions:
Our data highlight the superiority of a high-resolution FLAIR-DTI protocol combined with probabilistic tractography for brainstem tract imaging, which performs well at specifically delineating CNV fibers in the brainstem which make up the STr. This optimized DTI methodology may allow us to better examine the pathophysiology of TN and generate brain stem specific biomarkers in TN or other facial pain conditions.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Novel Imaging Acquisition Methods:
Diffusion MRI 1
Keywords:
MRI
Nerves
Pain
STRUCTURAL MRI
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Provide references using APA citation style.
Lambru, G., Zakrzewska, J., & Matharu, M. (2021). Trigeminal neuralgia: A practical guide. Practical Neurology, 21(5), 392–402. https://doi.org/10.1136/practneurol-2020-002782
Leemans, A. (1970, January 1). ExploreDTI: A graphical toolbox for processing, analyzing, and visualizing diffusion mr data. ScienceOpen. https://www.scienceopen.com/document?vid=bd88cd8e-827b-4597-8616-0d59d8c98ab9
Li, T., Sheng, L., Chunyan, C., Haoqiang, H., Kangqiang, P., Xiao, G., & Lizhi, L. (2017). The significance of diffusion tensor magnetic resonance imaging for patients with nasopharyngeal carcinoma and trigeminal nerve invasion. Medicine, 96(6). https://doi.org/10.1097/md.0000000000006072
Neetu, S., Sunil, K., Ashish, A., Jayantee, K., & Usha Kant, M. (2015). Microstructural abnormalities of the trigeminal nerve by diffusion-tensor imaging in trigeminal neuralgia without neurovascular compression. The Neuroradiology Journal, 29(1), 13–18. https://doi.org/10.1177/1971400915620439