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Title: SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography.
Austin Authors: Smith, Robert E;Tournier, Jacques-Donald;Calamante, Fernando;Connelly, Alan
Affiliation: Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
Centre for the Developing Brain, King's College London, London, United Kingdom
Imaging Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Department of Medicine, Northern Health, University of Melbourne, Melbourne, 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
Issue Date: 1-Oct-2015 2015-07-08
Publication information: NeuroImage 2015; 119: 338-51
Abstract: Diffusion MRI streamlines tractography allows for the investigation of the brain white matter pathways non-invasively. However a fundamental limitation of this technology is its non-quantitative nature, i.e. the density of reconstructed connections is not reflective of the density of underlying white matter fibres. As a solution to this problem, we have previously published the "spherical-deconvolution informed filtering of tractograms (SIFT)" method, which determines a subset of the streamlines reconstruction such that the streamlines densities throughout the white matter are as close as possible to fibre densities estimated using the spherical deconvolution diffusion model; this permits the use of streamline count as a valid biological marker of connection density. Particular aspects of its performance may have however limited its uptake in the diffusion MRI research community. Here we present an alternative to this method, entitled SIFT2, which provides a more logically direct and computationally efficient solution to the streamlines connectivity quantification problem: by determining an appropriate cross-sectional area multiplier for each streamline rather than removing streamlines altogether, biologically accurate measures of fibre connectivity are obtained whilst making use of the complete streamlines reconstruction.
DOI: 10.1016/j.neuroimage.2015.06.092
PubMed URL: 26163802
Type: Journal Article
Subjects: Diffusion
Magnetic Resonance Imaging
Structural connectivity
Appears in Collections:Journal articles

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