Hi, all,
I wanna to extract the fiber tracts of functional areas in the whole brain fibers.
I have searched some documents about registration, brain atlas, brain surface. However, I still feel very puzzle about the steps to extract the fibers.
I have already generated the “.tck” file of the whole brain by using the diffusion MRI dataset (not T1-WI, T2-WI and PD-WI datasets, I just ignore them). I also find someone does registration with T1 dataset. So I am more confused.
what inside my head is a lot of mess.
best,
Hi @SuperClear ,
That’s a lot of issues to go through in a forum… I recommend you read the papers below if you haven’t already. If things are still not clear, feel free to ask more specific questions - but otherwise it’s hard to know how to even begin answering such a broad set of questions…
RE Smith, JD Tournier, F Calamante and A Connelly,
NeuroImage , Feb 2013 15
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.
RE Smith, JD Tournier, F Calamante and A Connelly,
NeuroImage , Sep 2012
Diffusion MRI streamlines tractography suffers from a number of inherent limitations, one of which is the accurate determination of when streamlines should be terminated. Use of an accurate streamlines propagation mask from segmentation of an anatomical image confines the streamlines to the volume of the brain white matter, but does not take full advantage of all of the information available from such an image. We present a modular addition to streamlines tractography, which makes more effective use of the information available from anatomical image segmentation, and the known properties of the neuronal axons being reconstructed, to apply biologically realistic priors to the streamlines generated; we refer to this as "Anatomically-Constrained Tractography". Results indicate that some of the known false positives associated with tractography algorithms are prevented, such that the biological accuracy of the reconstructions should be improved, provided that state-of-the-art streamlines tractography methods are used.
RE Smith, JD Tournier, F Calamante and A Connelly,
NeuroImage , Jan 2015 01
Diffusion MRI streamlines tractography is increasingly being used to characterise and assess the structural connectome of the human brain. However, issues pertaining to quantification of structural connectivity using streamlines reconstructions are well-established in the field, and therefore the validity of any conclusions that may be drawn from these analyses remains ambiguous. We recently proposed a post-processing method entitled "SIFT: Spherical-deconvolution Informed Filtering of Tractograms" as a mechanism for reducing the biases in quantitative measures of connectivity introduced by the streamlines reconstruction method. Here, we demonstrate the advantage of this approach in the context of connectomics in three steps. Firstly, we carefully consider the model imposed by the SIFT method, and the implications this has for connectivity quantification. Secondly, we investigate the effects of SIFT on the reproducibility of structural connectome construction. Thirdly, we compare quantitative measures extracted from structural connectomes derived from streamlines tractography, with and without the application of SIFT, to published estimates drawn from post-mortem brain dissection. The combination of these sources of evidence demonstrates the important role the SIFT methodology has for the robust quantification of structural connectivity of the brain using diffusion MRI.
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Hi, Donald,
Thank you for your suggestion and recommendation.
Thanks again.
I really hope to find some clues on this problem.
