Reproducibility of tract segmentation between sessions using an unsupervised modelling-based approach

This work describes a reproducibility analysis of scalar water diffusion parameters, measured within white matter tracts segmented using a probabilistic shape modelling method. In common with previously reported neighbourhood tractography (NT) work, the technique optimises seed point placement for f...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2009-04, Vol.45 (2), p.377-385
Main Authors: Clayden, Jonathan D., Storkey, Amos J., Maniega, Susana Muñoz, Bastin, Mark E.
Format: Article
Language:English
Subjects:
Adult
Algorithms
Artificial Intelligence
Attention deficit hyperactivity disorder
Brain
Brain - anatomy & histology
Computer Simulation
Diffusion Magnetic Resonance Imaging - methods
Female
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Male
Methods
Models, Neurological
Nerve Fibers, Myelinated - ultrastructure
NMR
Nuclear magnetic resonance
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
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Summary:This work describes a reproducibility analysis of scalar water diffusion parameters, measured within white matter tracts segmented using a probabilistic shape modelling method. In common with previously reported neighbourhood tractography (NT) work, the technique optimises seed point placement for fibre tracking by matching the tracts generated using a number of candidate points against a reference tract, which is derived from a white matter atlas in the present study. No direct constraints are applied to the fibre tracking results. An Expectation–Maximisation algorithm is used to fully automate the procedure, and make dramatically more efficient use of data than earlier NT methods. Within-subject and between-subject variances for fractional anisotropy and mean diffusivity within the tracts are then separated using a random effects model. We find test–retest coefficients of variation (CVs) similar to those reported in another study using landmark-guided single seed points; and subject to subject CVs similar to a constraint-based multiple ROI method. We conclude that our approach is at least as effective as other methods for tract segmentation using tractography, whilst also having some additional benefits, such as its provision of a goodness-of-match measure for each segmentation.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2008.12.010