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SIFT: Spherical-deconvolution informed filtering of tractograms
Abstract
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.
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“Recently, a new method, called spherical-deconvolution informed filtering of tractograms (SIFT) has been proposed to reduce local streamline density bias by filtering the tractogram (i.e., selectively removing streamlines) to improve the fit between the overall distribution of streamlines and the FOD in every voxel, estimated from the diffusion-weighted image, which may improve accuracy of connect”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Smith et al. 2013
In the citing paper
“While in its original implementation SIFT (Smith et al., 2013) provides only the NOS adjusted based on voxel-wise spherical deconvolution, its evolution SIFT2 (Smith et al., 2015b) optimizes per-streamline cross-section multipliers to match a whole-brain tractogram to fixel-wise fiber densities and thus provides a value for each streamline that can be used (for example) to define the edge weights”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Smith et al., 2013
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Metadata
- Authors
- Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Smith, Robert E.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 67
- Pages
- 298-312
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1995138343
- Cited by
- 752
- References
- 35
- Citations by year
- 2026: 66 · 2025: 82 · 2024: 82 · 2023: 78 · 2022: 69 · 2021: 91 · 2020: 80 · 2019: 49 · 2018: 53 · 2017: 35 · 2016: 29 · 2015: 18 · 2014: 11 · 2013: 9
- FWCI
- 9.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- National Health and Medical Research Council (NHMRC) of Australia
- Australian Research Council
- Austin Health
- Dowd Foundation Research Scholarship for Neuroscience
- Victorian Government's Operational Infrastructure Support Program
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- tractography, spherical deconvolution, diffusion MRI, structural connectivity, reconstruction bias
- MeSH terms
- Algorithms, Brain, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Nerve Fibers, Myelinated, Sensitivity and Specificity, Software, Reproducibility of Results, Imaging, Three-Dimensional, Diffusion Tensor Imaging, Connectome
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