SIFT: Spherical-deconvolution informed filtering of tractograms
By Rowan J. Smith, Jacques-Donald Tournier, Fernando Calamante, Alan Connelly. NeuroImage, 2013
755 citations recorded by the source corpus.
Subfields: Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/aae77dcd9534d6679e4de028878b958679aeb85e
How researchers see this paper
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In Radiology, Nuclear Medicine and Imaging
- Jacques-Donald Tournier
- Ben Jeurissen
- Alan Connelly
- Alexander Leemans
- Maxime Descoteaux
- Rowan J. Smith
- Rachid Deriche
- Susumu Mori
Citations in the corpus
- Towards quantitative connectivity analysis: reducing tractography biases (2014, positive): “Recently, Smith et al. (2013) proposed a 63 method called Spherical-deconvolution informed filtering (SIFT) to reduce local bias in the streamlines density.”
- Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres (2015, methodology): “To identify all fi xels in the FOD template, we segment each FOD lobe using the method outlined in Smith et al. (2013).”
- Investigating white matter fibre density and morphology using fixel-based analysis (2017, methodology): “Briefly, FOD lobes are first segmented based on the peaks and troughs of the FOD, and the apparent FD of each lobe is calculated by nonparametric numerical integration using a dense sampling of the FOD over a hemisphere (Smith et al., 2013).”
- Diffusion MRI fiber tractography of the brain (2019, methodology): “Closely related to global tracking is the concept of filtering a whole‐brain tractogram.(45,74-77) While global fiber tracking uses a bottom‐up approach, where a tractogram is directly constructed in one global optimization routine to match the measured dMRI data, tractogram filtering adopts a top‐d”
- Network‐based rTMS to modulate working memory: The difficult choice of effective parameters for online interventions (2021, methodology): “After tracts were generated, they were filtered using spherical-deconvolution informed filtering of tractograms (Smith et al., 2013).”