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

• Published in: NeuroImage (Orlando, Fla.) Vol. 45; no. 2; pp. 377 - 385
• Main Authors: Clayden, Jonathan D., Storkey, Amos J., Maniega, Susana Muñoz, Bastin, Mark E.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2009; Elsevier Limited
• 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
• DOI: 10.1016/j.neuroimage.2008.12.010
• ISSN: 1053-8119; 1095-9572

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.