title: SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography.
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.
authors.affiliation: Imaging Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia. Electronic address: robert.smith@florey.edu.au.
authors.forename: Robert E
authors.initials: RE
authors.lastname: Smith
authors.affiliation: Imaging Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia; Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for the Developing Brain, King's College London, London, United Kingdom; Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
authors.forename: Jacques-Donald
authors.initials: JD
authors.lastname: Tournier
authors.affiliation: Imaging Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia; Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Fernando
authors.initials: F
authors.lastname: Calamante
authors.affiliation: Imaging Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia; Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Alan
authors.initials: A
authors.lastname: Connelly
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D066127:White Matter / Q000033:anatomy & histology*
keywords: Diffusion; Magnetic resonance imaging; SIFT; Streamlines; Structural connectivity; Tractography