Re: Affine issue with atlas of 80 Bundles in MNI space
Yes, you can correct the affine by using the StatefulTractogram and the
affine of the data that generated the tracks or the original tractogram
https://dipy.org/documentation/1.0.0./examples_built/streamline_formats/#exa...
On Thu, Nov 21, 2019 at 12:28 PM Giulia Berto'
Hi,
I am trying to use the atlas of 80 bundles you distribute here https://figshare.com/articles/Advanced_Atlas_of_80_Bundles_in_MNI_space/7375... https://figshare.com/articles/Advanced_Atlas_of_80_Bundles_in_MNI_space/7375..., but I am encountering some issues.
It is written that the files are in MNI space ICBM 2009a, which has affine equal to array([[ 1., 0., 0., -98.], [ 0., 1., 0., -134.], [ 0., 0., 1., -72.], [ 0., 0., 0., 1.]])
However, all the trk files have affine equal to the identity matrix. Is there a reason why you stored in the header of the trk files the identity matrix? This for example prevents their correct visualization in TrackVis.
[image: ifof_r_dipy_trackvis.png]
Another question: the file whole_brain_MNI.trk, besides having the same issue with the affine, has 144678 streamlines. Is it the same whole brain tractogram Yeh is referring to in their paper? In that paper they obtained a tractogram of 550000 streamlines. Or is it maybe the union of the 80 bundles?
Thank you a lot for your help, best regards, Giulia
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participants (3)
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Ariel Rokem
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Eleftherios Garyfallidis
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Giulia Berto'