Abstract
Background and PurposeDiffusion MRI tractography enables to investigate white matter pathways noninvasively by reconstructing estimated fiber pathways. However, such tractograms remain biased and nonquantitative. Several techniques have been proposed to reestablish the link between tractography and tissue microstructure by modeling the diffusion signal or fiber orientation distribution (FOD) with the given tractogram and optimizing each fiber or compartment contribution according to the diffusion signal or FOD. Nevertheless, deriving a reliable quantification of connectivity strength between different brain areas is still a challenge. Moreover, evaluating the quality of a tractogram and measuring the possible error sources contained in a specific reconstructed fiber bundle also remains difficult. Lastly, all of these optimization techniques fail if specific fiber populations within a tractogram are underrepresented, for example, due to algorithmic constraints, anatomical properties, fiber geometry or seeding patterns.MethodsIn this work, we propose an approach which enables the inspection of the quality of a tractogram optimization by evaluating the residual error signal and its FOD representation. The automated fiber quantification (AFQ) is applied, whereby the framework is extended to reflect not only scalar diffusion metrics along a fiber bundle, but also directionally dependent FOD amplitudes along and perpendicular to the fiber direction. Furthermore, we also present an up‐sampling procedure to increase the number of streamlines of a given fiber population. The introduced error metrics and fiber up‐sampling method are tested and evaluated on single‐shell diffusion data sets of 16 healthy volunteers.Results and ConclusionAnalyzing the introduced error measures on specific fiber bundles shows a considerable improvement in applying the up‐sampling method. Additionally, the error metrics provide a useful tool to spot and identify potential error sources in tractograms.
Highlights
The performance of tracking algorithms has significantly improved by considering the information contained in orientation distribution functions (ODF) or fiber orientation distribution (FOD), especially in regions with complex fiber configurations (Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007; Fillard et al, 2011; Tournier, Mori, & Leemans, 2011)
We have introduced a tool to investigate the quality of a tractogram by further inspecting the directionally dependent error signal between the signal prediction and the measured diffusion signal along reconstructed fiber bundles
The overall mean fit error averaged over all the white-matter voxels and all subjects showed only small, but significant changes comparing the initial (AFQ, WB) with the up-sampled (AFQUP, WBUP) fiber tractograms
Summary
Diffusion magnetic resonance imaging (Le Bihan et al, 1986) is a compelling tool for probing microscopic tissue properties and diffusion tensor imaging (DTI) has become a popular model to inspect white matter architecture. Tractograms remain biased by algorithmic-specific parameters, that is, stopping criteria, curvature thresholds, seed point distribution, and the choice of the tracking algorithm itself, as well as partial volume effects of different fiber populations or various tissue types within the acquired data voxels This complicates the estimation of reliable tractograms and the extraction of biologically meaningful connectivity measures between brain areas which are a crucial requirement for an accurate, quantitative connectome across different populations (Jbabdi & Johansen-Berg, 2011; Jones, 2010; Jones, Knösche, & Turner, 2012). We further show that these metrics, combined with a newly introduced error FA, allow a better interpretation of the directional error distribution These are important steps toward interpreting fiber weights from a tractogram optimization in a quantitative way to, for example, construct a more meaningful connectivity measure in a connectome. Analyzing the introduced error measures on specific fiber bundles shows the benefit of using up-sampled fiber bundles
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