Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/11630
Title: SIFT: Spherical-deconvolution informed filtering of tractograms.
Austin Authors: Smith, Robert E;Tournier, Jacques-Donald;Calamante, Fernando;Connelly, Alan
Affiliation: Brain Research Institute, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Issue Date: 11-Dec-2012
Publication information: Neuroimage 2012; 67(): 298-312
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.
Gov't Doc #: 23238430
URI: http://ahro.austin.org.au/austinjspui/handle/1/11630
DOI: 10.1016/j.neuroimage.2012.11.049
URL: https://pubmed.ncbi.nlm.nih.gov/23238430
Type: Journal Article
Subjects: Algorithms
Brain.cytology
Connectome.methods
Diffusion Tensor Imaging.methods
Humans
Image Enhancement.methods
Image Interpretation, Computer-Assisted.methods
Imaging, Three-Dimensional.methods
Nerve Fibers, Myelinated.ultrastructure
Reproducibility of Results
Sensitivity and Specificity
Software
Appears in Collections:Journal articles

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