SIFT: Spherical-deconvolution informed filtering of tractograms

TY - JOUR

T1 - SIFT

T2 - Spherical-deconvolution informed filtering of tractograms

AU - Smith, Robert E.

AU - Tournier, Jacques-Donald

AU - Calamante, Fernando

AU - Connelly, Alan

PY - 2013/2/15

Y1 - 2013/2/15

N2 - 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.

AB - 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.

KW - Magnetic resonance imaging

KW - Diffusion MRI

KW - Fibre-tracking

KW - Tractography

KW - Streamlines

KW - DIFFUSION-WEIGHTED MRI

KW - INDUCED DISTORTION

KW - SPIN-ECHO

KW - RECONSTRUCTION

KW - ALGORITHM

KW - DENSITY

KW - IMAGES

KW - PROJECTOME

KW - FRAMEWORK

U2 - 10.1016/j.neuroimage.2012.11.049

DO - 10.1016/j.neuroimage.2012.11.049

M3 - Article

SN - 1053-8119

VL - 67

SP - 298

EP - 312

JO - NeuroImage

JF - NeuroImage

ER -