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SIFT: Spherical-deconvolution informed filtering of tractograms

Diffusion MRI allows the structural connectivity of the whole brain (the ‘tractogram’) to be estimated in vivo non-invasively using streamline tractography. The biological accuracy of these data sets is however limited by the inherent biases associated with the reconstruction method. Here we propose... Full description

Journal Title: NeuroImage 15 February 2013, Vol.67, pp.298-312
Main Author: Smith, Robert E
Other Authors: Tournier, Jacques-Donald , Calamante, Fernando , Connelly, Alan
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1053-8119 ; E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2012.11.049
Link: https://www.sciencedirect.com/science/article/pii/S1053811912011615
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recordid: elsevier_sdoi_10_1016_j_neuroimage_2012_11_049
title: SIFT: Spherical-deconvolution informed filtering of tractograms
format: Article
creator:
  • Smith, Robert E
  • Tournier, Jacques-Donald
  • Calamante, Fernando
  • Connelly, Alan
subjects:
  • Magnetic Resonance Imaging
  • Diffusion Mri
  • Fibre-Tracking
  • Tractography
  • Streamlines
  • Medicine
ispartof: NeuroImage, 15 February 2013, Vol.67, pp.298-312
description: Diffusion MRI allows the structural connectivity of the whole brain (the ‘tractogram’) to be estimated in vivo non-invasively using streamline tractography. The biological accuracy of these data sets is however limited by the inherent biases associated with the reconstruction method. Here we propose a method to retrospectively improve the accuracy of these reconstructions, by selectively filtering out streamlines from the tractogram in a manner that improves the fit between the streamline reconstruction and the underlying diffusion images. This filtering is guided by the results of spherical deconvolution of the diffusion signal, hence the acronym SIFT: spherical-deconvolution informed filtering of tractograms. Data sets processed by this algorithm show a marked reduction in known reconstruction biases, and improved biological plausibility. Emerging methods in diffusion MRI, particularly those that aim to characterise and compare the structural connectivity of the brain, should benefit from the improved accuracy of the reconstruction. ► Novel method for selective filtering of whole-brain fibre-tracking data ► Reduces reconstruction biases of streamline tractography ► Improved biological plausibility of connectome reconstruction ► Better interpretability of structural connectivity between regions
language: eng
source:
identifier: ISSN: 1053-8119 ; E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2012.11.049
fulltext: fulltext
issn:
  • 1053-8119
  • 10538119
  • 1095-9572
  • 10959572
url: Link


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subjectMagnetic Resonance Imaging ; Diffusion Mri ; Fibre-Tracking ; Tractography ; Streamlines ; Medicine
descriptionDiffusion MRI allows the structural connectivity of the whole brain (the ‘tractogram’) to be estimated in vivo non-invasively using streamline tractography. The biological accuracy of these data sets is however limited by the inherent biases associated with the reconstruction method. Here we propose a method to retrospectively improve the accuracy of these reconstructions, by selectively filtering out streamlines from the tractogram in a manner that improves the fit between the streamline reconstruction and the underlying diffusion images. This filtering is guided by the results of spherical deconvolution of the diffusion signal, hence the acronym SIFT: spherical-deconvolution informed filtering of tractograms. Data sets processed by this algorithm show a marked reduction in known reconstruction biases, and improved biological plausibility. Emerging methods in diffusion MRI, particularly those that aim to characterise and compare the structural connectivity of the brain, should benefit from the improved accuracy of the reconstruction. ► Novel method for selective filtering of whole-brain fibre-tracking data ► Reduces reconstruction biases of streamline tractography ► Improved biological plausibility of connectome reconstruction ► Better interpretability of structural connectivity between regions
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Diffusion MRI allows the structural connectivity of the whole brain (the ‘tractogram’) to be estimated in vivo non-invasively using streamline tractography. The biological accuracy of these data sets is however limited by the inherent biases associated with the reconstruction method. Here we propose a method to retrospectively improve the accuracy of these reconstructions, by selectively filtering out streamlines from the tractogram in a manner that improves the fit between the streamline reconstruction and the underlying diffusion images. This filtering is guided by the results of spherical deconvolution of the diffusion signal, hence the acronym SIFT: spherical-deconvolution informed filtering of tractograms. Data sets processed by this algorithm show a marked reduction in known reconstruction biases, and improved biological plausibility. Emerging methods in diffusion MRI, particularly those that aim to characterise and compare the structural connectivity of the brain, should benefit from the improved accuracy of the reconstruction.

► Novel method for selective filtering of whole-brain fibre-tracking data ► Reduces reconstruction biases of streamline tractography ► Improved biological plausibility of connectome reconstruction ► Better interpretability of structural connectivity between regions

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Diffusion MRI allows the structural connectivity of the whole brain (the ‘tractogram’) to be estimated in vivo non-invasively using streamline tractography. The biological accuracy of these data sets is however limited by the inherent biases associated with the reconstruction method. Here we propose a method to retrospectively improve the accuracy of these reconstructions, by selectively filtering out streamlines from the tractogram in a manner that improves the fit between the streamline reconstruction and the underlying diffusion images. This filtering is guided by the results of spherical deconvolution of the diffusion signal, hence the acronym SIFT: spherical-deconvolution informed filtering of tractograms. Data sets processed by this algorithm show a marked reduction in known reconstruction biases, and improved biological plausibility. Emerging methods in diffusion MRI, particularly those that aim to characterise and compare the structural connectivity of the brain, should benefit from the improved accuracy of the reconstruction.

► Novel method for selective filtering of whole-brain fibre-tracking data ► Reduces reconstruction biases of streamline tractography ► Improved biological plausibility of connectome reconstruction ► Better interpretability of structural connectivity between regions

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date2013-02-15