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

Abstract

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.

A General Factor of Brain White Matter Integrity Predicts Information Processing Speed in Healthy Older People

“…The BEDPOST/ProbTrack tractography algorithm (Behrens et al, 2007) with a two-fiber model and 5000 streamlines was used to reconstruct tracts of interest. An automatic tract selection method with good reproducibility (Clayden et al, 2009a), based on a model of tract topology (Clayden et al, 2007;Bastin et al, 2008), was used to generate equivalent tracts of interest in each subject. This technique, termed probabilistic neighborhood tractography, optimizes the choice of seed point for tractography by estimating the best matching tract from a series of candidates against a reference tract derived from a digital human white matter atlas (Hua et al, 2008), as described by Muñoz Maniega et al (2008).…”

Shape Modelling for Tract Selection

“…The model parameters are fitted using an Expectation-Maximisation (EM) algorithm, the E-step of which calculates a posterior probability of each tract representing the best match to the reference tract [5]. All tracts are assumed to be a priori equiprobable matches.…”

Processing and Visualization of Diffusion MRI

“…Alternatively, the tract seeding and other algorithm parameters could be optimized until the tracts (data driven) approach the reference (data prior) [30]. Since this requires prespecifying such a reference bundle, information that may be unavailable or difficult to obtain, one could even incorporate the formulation of the reference bundle into the optimization procedure itself [31].…”

“…Alternatively, one can begin with the end in mind by registering a reference fiber bundle template to patients thus obviating any need for later spatial normalization or correspondence [31].…”