2007
DOI: 10.1016/j.neuroimage.2007.02.016
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Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution

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Cited by 2,013 publications
(2,029 citation statements)
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References 34 publications
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“…Each streamline was propagated using the second‐order integration over fiber orientation distributions (iFOD2) probabilistic tractography algorithm (Tournier, Calamante, & Connelly, 2010). Streamlines were guided by fiber orientations inferred using spherical deconvolution with a maximum harmonic order ( l max ) of 4 (Tournier, Calamante, & Connelly, 2007). Propagation was terminated if either a minimum angle threshold of 45° was violated or the streamline propagated beyond the white matter mask.…”
Section: Methodsmentioning
confidence: 99%
“…Each streamline was propagated using the second‐order integration over fiber orientation distributions (iFOD2) probabilistic tractography algorithm (Tournier, Calamante, & Connelly, 2010). Streamlines were guided by fiber orientations inferred using spherical deconvolution with a maximum harmonic order ( l max ) of 4 (Tournier, Calamante, & Connelly, 2007). Propagation was terminated if either a minimum angle threshold of 45° was violated or the streamline propagated beyond the white matter mask.…”
Section: Methodsmentioning
confidence: 99%
“…Constrained spherical deconvolution (CSD) (Tournier et al, 2007) was used to model multiple fibre orientations, and probabilistic tracking (Behrens et al, 2003) was performed using the 2 nd order integration over fibre orientation distributions (iFOD2) algorithm . Whole-brain (or wholephantom) tracking was performed by randomly seeding throughout the brain or throughout the numerical phantom (i.e.…”
Section: Fibre-trackingmentioning
confidence: 99%
“…seed points were randomly defined in continuous space covering the whole brain or phantom) using the following relevant parameters: 1 mm step-size, maximum angle between steps = 45º, 3 FOD samples/step, any track with length < 10 mm (for the in vivo data) or < 4 mm (for the in silico data) was discarded, termination criteria: exit the brain (or the numerical phantom) or when the CSD fibre-orientation distribution amplitude was < 0.1. The primary parameter of interest in CSD is the maximum harmonic order lmax (which determines the 'sharpness' of the fibre orientation distributions) (Tournier et al, 2004;Tournier et al, 2007;Tournier et al, 2008); lmax = 10 (for the in vivo data) and 8 (for the in silico data) were employed in this study.…”
Section: Fibre-trackingmentioning
confidence: 99%
“…This technique theoretically provides an advantage over traditional diffusion tensor imaging (DTI) in the study of intertwined and crossing fiber tracts (Frank, 2002; Tournier, Calamante, & Connelly, 2007; Tournier, Calamante, Gadian, & Connelly, 2004) which are common among cerebellar peduncular tracts. Although several techniques to assess white matter development have been developed in addition to diffusion MR tractography (Ball et al., 2013; O'Muircheartaigh et al., 2014), advantages of diffusion tractography include the detection of three‐dimensional fiber bundle pathways.…”
Section: Introductionmentioning
confidence: 99%