Facial Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition b -Values and Single- and Multifiber Tracking Strategies

HYPOTHESIS: This study investigates the impact of different diffusion magnetic imaging (dMRI) acquisition settings and mathematical fiber models on tractography performance for depicting cranial nerve (CN) VII in healthy young adults. BACKGROUND: The aim of this study is to optimize visualization of CN VII for preoperative assessment in surgeries near the nerve in the cerebellopontine angle, reducing surgery-associated complications. The study analyzes 100 CN VII in dMRI images from the Human Connectome Project, using three separate sets with different b values ( b = 1,000 s/mm 2 , b =2,000 s/mm 2 , b =3,000 s/mm 2 ) and four different tractography methods, resulting in 1,200 tractographies analyzed. RESULTS: The results show that multifiber and free water (FW) compartment models produce significantly more streamlines than single-fiber tractography. The addition of an FW compartment significantly increases the mean streamline fractional anisotropy (FA). Expert quality ratings showed that the highest rated tractography was the 1 tensor (1T) method without FW at b values of 1,000 s/mm2. CONCLUSIONS: In this young and healthy cohort, best tractography results are obtained by using a 1T model without a FW compartment in b =1,000 diffusion MR images. The FW compartment increased the contrast between streamlines and cerebrospinal fluid (higher mean streamline FA). This finding may help ongoing research to improve CN VII tractography results in tumor cases where the nerve is often stretched and thinned by the tumor.

Publication Details

Journal
Zurich Open Repository and Archive (University of Zurich)
Published
2024-10-01
DOI
https://doi.org/10.5167/uzh-262308
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
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article

Facial Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition b -Values and Single- and Multifiber Tracking Strategies

Zurich Open Repository and Archive (University of Zurich)
Advanced Neuroimaging Techniques and Applications
article

Facial Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition b -Values and Single- and Multifiber Tracking Strategies

article en

Abstract

HYPOTHESIS: This study investigates the impact of different diffusion magnetic imaging (dMRI) acquisition settings and mathematical fiber models on tractography performance for depicting cranial nerve (CN) VII in healthy young adults. BACKGROUND: The aim of this study is to optimize visualization of CN VII for preoperative assessment in surgeries near the nerve in the cerebellopontine angle, reducing surgery-associated complications. The study analyzes 100 CN VII in dMRI images from the Human Connectome Project, using three separate sets with different b values ( b = 1,000 s/mm 2 , b =2,000 s/mm 2 , b =3,000 s/mm 2 ) and four different tractography methods, resulting in 1,200 tractographies analyzed. RESULTS: The results show that multifiber and free water (FW) compartment models produce significantly more streamlines than single-fiber tractography. The addition of an FW compartment significantly increases the mean streamline fractional anisotropy (FA). Expert quality ratings showed that the highest rated tractography was the 1 tensor (1T) method without FW at b values of 1,000 s/mm2. CONCLUSIONS: In this young and healthy cohort, best tractography results are obtained by using a 1T model without a FW compartment in b =1,000 diffusion MR images. The FW compartment increased the contrast between streamlines and cerebrospinal fluid (higher mean streamline FA). This finding may help ongoing research to improve CN VII tractography results in tumor cases where the nerve is often stretched and thinned by the tumor.

Zurich Open Repository and Archive (University of Zurich)
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Advanced Neuroimaging Techniques and Applications
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Facial Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition b -Values and Single- and Multifiber Tracking Strategies · Zurich Open Repository and Archive (University of Zurich) (2024) | TGRS Research Map | TGRS