Dear Experts,
Command :
tckgen -act 5tt_coreg.mif -backtrack \
$wmFOD $tckFName -seed_gmwmi gmwmi.mif -seed_image maskImage.nii.gz -select 10M -cutoff 0.05 --force
Queries :
1.Can we use two different seed options (seed_image and seed_gmwmi ) in one tckgen command run?
a) If yes does the order of the seed options influence the outcome ?
b) Can we generate the 10M streamlines, with 5M streamlines contribution from each option?
2.Generating 5M streamlines separately with the above seed options and merging both would be equivalent to above mechanism ?
3.Which above approaches could be efficient technically, if next step was to use tckedit with SIFT2 based filtering?
Thank you,
Suhail
Hi @suhail,
The approach you propose is perfectly valid, it was implemented here. You can do as many parameter permutations as you want.
In you specific scenario, I would run two tckgen commands each one with 5M streamlines and a different seeding strategy, then combine them with tckedit and run tcksift2 in the combined tractogram. I hope this helps.
Best regards,
Manuel
Hi Manuel,
Thank you for providing the insights on possible implementation and also directing to the ensemble tractography concept.
Best regards,
Suhail
Hi Manuel and others,
One concern we had was choice of #streamlines for each approach. 10M for whole brain is fine. For the seed_image, if we choose too much, it will be artifically boosted (thalamus is our seed which is much smaller than whole GM). One thought is to do a volume of GM/volume of thalamus and use this ratio to divide 10M and use that. In any case, how much mismatch can SIFT2 handle as we will SIFT2 after combining the tracks from the 2 methods.
We are already finding (thanks to rsmith comment in another thread) that seed_dynamic is much better and fusion is not helping the big nuclei but small nuclei seem to improve which makes sense but we need to look at this on 10 or 20 subjects and see if it holds.
Thanks,
manoj