Article

White matter tractography using tensor deflection

University of Utah, Salt Lake City, Utah, United States
Human Brain Mapping (Impact Factor: 5.97). 04/2003; 18(4):306-21. DOI: 10.1002/hbm.10102
Source: PubMed

ABSTRACT

Diffusion tensor MRI provides unique directional diffusion information that can be used to estimate the patterns of white matter connectivity in the human brain. In this study, the behavior of an algorithm for white matter tractography is examined. The algorithm, called TEND, uses the entire diffusion tensor to deflect the estimated fiber trajectory. Simulations and imaging experiments on in vivo human brains were performed to investigate the behavior of the tractography algorithm. The simulations show that the deflection term is less sensitive than the major eigenvector to image noise. In the human brain imaging experiments, estimated tracts were generated in corpus callosum, corticospinal tract, internal capsule, corona radiata, superior longitudinal fasciculus, inferior longitudinal fasciculus, fronto-occipital fasciculus, and uncinate fasciculus. This approach is promising for mapping the organizational patterns of white matter in the human brain as well as mapping the relationship between major fiber trajectories and the location and extent of brain lesions.

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Available from: Victor M Haughton, Dec 13, 2013
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    • "and probabilistic [e.g., Parker et al., 2003; Behrens et al., 2003] tracking. In the first case, tracts propagate outward from seed points until some stopping criterion is reached; propagation through a given voxel may, for example, be oriented parallel to the local principal eigenvector [e.g., Mori et al., 1999; Taylor et al., 2012] or to some weighted average of neighboring eigenvectors [e.g., Lazar et al., 2003]. The deterministic output is a set of tracts embedded within the brain volume—that is, a 1D sequence of points, each of which has 3 spatial coordinates and possibly attached properties such as DTI parameter values. "
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    DESCRIPTION: Updates and examples of combining FATCAT, SUMA and AFNI, including: a new "mini-probabilistic" approach to tractography (as an improvement to the standard deterministic methodology); descriptions of new user-interactive visualization tools, particularly for functional and structural network connectivity, combining AFNI and SUMA; and approaches for performing group analysis of FMRI/DTI networks using 3dMVM and FATCAT command line tools.
    Full-text · Research · Oct 2015
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    • "and probabilistic [e.g., Parker et al., 2003; Behrens et al., 2003] tracking. In the first case, tracts propagate outward from seed points until some stopping criterion is reached; propagation through a given voxel may, for example, be oriented parallel to the local principal eigenvector [e.g., Mori et al., 1999; Taylor et al., 2012] or to some weighted average of neighboring eigenvectors [e.g., Lazar et al., 2003]. The deterministic output is a set of tracts embedded within the brain volume—that is, a 1D sequence of points, each of which has 3 spatial coordinates and possibly attached properties such as DTI parameter values. "
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    ABSTRACT: Brain connectivity investigations are becoming increasingly multimodal, and they present challenges for quantitatively characterizing and interactively visualizing data. Here we present a new set of network-based software tools for combining functional and anatomical connectivity from MRI data. The computational tools are available as part of FATCAT, a toolbox that interfaces with AFNI and SUMA for interactive queries and visualization. This includes a novel tractographic "mini-probabilistic" approach to improve streamline tracking in networks. We show how one obtains more robust tracking results for determining white matter connections by utilizing the uncertainty of the estimated DTI parameters and a few Monte Carlo iterations. This allows for thresholding based on the number of connections between target pairs in order to reduce the presence of tracts likely due to noise. In order to assist users in combining data, we describe an interface for navigating and performing queries in 2D and 3D for data defined over voxel, surface, tract, and graph domains. These varied types of information can be visualized simultaneously and the queries performed interactively using SUMA and AFNI. The methods have been designed to increase the user's ability to visualize and combine FMRI and DTI modalities, particularly in the context of single-subject inferences (for example, in deep brain stimulation studies). Finally, we present a multivariate framework for statistically modeling network-based features in group analysis, which can be implemented for both functional and structural studies.
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    • "For tracking, an angle threshold of 45 degrees was set. The Interpolated Streamline was selected as the propagation algorithm[7]. 2) MRtrix3[8]provides a set of tools to perform diffusion-weighted MRI white matter tractography in the presence of crossing fibers, using a Constrained Spherical Deconvolution modelization910and both probabilistic and deterministic streamline tracking algorithms[11]. These applications were written from scratch by the authors in C++, using the functionality provided by the GNU Scientific Library, and Qt. "
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