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• W2091632017 endingPage "89" @default.
• W2091632017 startingPage "81" @default.
• W2091632017 abstract "Probability mapping of connectivity is a powerful tool to determine the fibre structure of white matter in the brain. Probability maps are related to the degree of connectivity to a chosen seed area. In many applications, however, it is necessary to isolate a fibre bundle that connects two areas. A frequently suggested solution is to select curves, which pass only through two or more areas. This is very inefficient, especially for long-distance pathways and small areas. In this paper, a novel probability-based method is presented that is capable of extracting neuronal pathways defined by two seed points. A Monte Carlo simulation based tracking method, similar to the Probabilistic Index of Connectivity (PICo) approach, was extended to preserve the directional information of the main fibre bundles passing a voxel. By combining two of these extended visiting maps arising from different seed points, two independent parameters are determined for each voxel: the first quantifies the uncertainty that a voxel is connected to both seed points; the second represents the directional information and estimates the proportion of fibres running in the direction of the other seed point (connecting fibre) or face a third area (merging fibre). Both parameters are used to calculate the probability that a voxel is part of the bundle connecting both seed points. The performance and limitations of this DTI-based method are demonstrated using simulations as well as in vivo measurements." @default.
• W2091632017 created "2016-06-24" @default.
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• W2091632017 date "2008-10-01" @default.
• W2091632017 modified "2023-10-06" @default.
• W2091632017 title "Connecting and merging fibres: Pathway extraction by combining probability maps" @default.
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• W2091632017 doi "https://doi.org/10.1016/j.neuroimage.2008.06.023" @default.
• W2091632017 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18644243" @default.
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