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Title: Track-weighted imaging methods: extracting information from a streamlines tractogram
Austin Authors: Calamante, Fernando
Affiliation: Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Department of Medicine, Northern Health, University of Melbourne, Melbourne, Victoria, Australia
Issue Date: Aug-2017 2017-02-08
Publication information: Magma (New York, N.Y.) 2017; 30(4): 317-335
Abstract: A whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information from the tractogram. One such approach is based on exploiting the information contained in the tractogram to generate track-weighted (TW) images. In the track-weighted imaging (TWI) approach, a very large number of streamlines are often generated throughout the brain, and an image is then computed based on properties of the streamlines themselves (e.g. based on the number of streamlines in each voxel, or their average length), or based on the values of an associated image (e.g. a diffusion anisotropy map, a T2 map) measured at the coordinates of the streamlines. This review article describes various approaches used to generate TW images and discusses the flexible formalism that TWI provides to generate a range of images with very different contrast, as well as the super-resolution properties of the resulting images. It also explains how this approach provides a powerful means to study structural and functional connectivity simultaneously. Finally, a number of key issues for its practical implementation are discussed.
DOI: 10.1007/s10334-017-0608-1
ORCID: 0000-0002-7550-3142
Journal: Magma (New York, N.Y.)
PubMed URL: 28181027
Type: Journal Article
Subjects: Connectivity
Appears in Collections:Journal articles

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