Great, thank you!
Does then the example "Automatic Fiber Bundle Extraction with RecoBundles"
here https://dipy.org/documentation/1.0.0./examples_built/bundle_extraction/
need
to be updated? I guess that sft_atlas = load_trk(atlas_file, "same",
bbox_valid_check=False) needs a reference rather that "same".
Thank you.
best,
Giulia
On Thu, Nov 21, 2019 at 6:30 PM Eleftherios Garyfallidis
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'
wrote: 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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