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Framework to evaluate direction dependent diffusion properties

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Schreiber,  Jan
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons22933

Riffert,  Till
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons19530

Anwander,  Alfred
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Knösche,  Thomas R.
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Schreiber, J., Riffert, T., Anwander, A., & Knösche, T. R. (2012). Framework to evaluate direction dependent diffusion properties. Poster presented at 18th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2012), Beijing, China.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-A194-0
Abstract
Diffusion MRI is a non-invasive method that potentially gives insight into the brain’s white matter structure regarding the pathway of connections and properties of the axons. Here, we propose a novel global tractography method named Plausibility Tracking that provides the most plausible pathway, modeled as a smooth spline curve, between two locations in the brain. Compared to other tractography methods, Plausibility Tracking combines the more complete connectivity pattern of probabilistic tractography with smooth tracks that are globally optimized using the fiber orientation density function and hence are relatively robust against local noise and error propagation. Additionally, it provides reliable local directions all along the fiber pathways which makes it especially interesting for tract-based analysis in combination with direction dependent indices of diffusion MRI. For this purpose, we propose a framework for the assessment and comparison of diffusion derived tissue properties, based on Plausibility Tracking, atlas-guided parameterization of tract representation and advanced bundle-specific indices describing fiber density, fiber spread and white matter complexity. We explore the new method using real data and show that it allows for a more specific interpretation of the white matter’s microstructure compared to rotationally invariant indices derived from the diffusion tensor.