I’m a beginner to mrTrix3, and I use it for fiber tracking. But after all what I’ve done,
tckgen result doesn’t seem very well.
I use ‘dtiImportFibersMrtrix’ (vistasoft/dtiImportFibersMrtrix.m at master · vistalab/vistasoft · GitHub) to read the result .tck file, and find that the fibers are short and messy, doesn’t show fiber bundle.
As I’m going to process data with AFQ, I’m not quite sure about the
tckgen result. Could anyone give me suggestion or guide?
The commands I have used:
5ttgen fsl t1.mif 5tt.mif
5tt2gmwmi 5tt.mif gmwmSeed.mif
dwibiascorrect -ants dwi_align.mif dwi_unbiased.mif
dwi2mask dwi_unbiased.mif - | maskfilter - dilate mask.mif
dwi2response msmt_5tt dwi_unbiased.mif 5tt.mif wm.txt gm.txt csf.txt
dwi2fod msmt_csd dwi_unbiased.mif wm.txt wmfod.mif
mtnormalise wmfod.mif wmfod_norm.mif -mask mask.mif
tckgen -act 5tt.mif -backtrack -seed_image -nthreads 4 -select 200k wmfod_norm.mif track_brain.tck
sorry, a little change:
tckgen -act 5tt.mif -backtrack -seed_gmwmi gmwmSeed.mif -nthreads 4 -select 200k wmfod_norm.mif track_brain.tck
Unfortunately such issues are very difficult to diagnose from just the streamlines data alone. What would help is more information about your data, and some images showing the data from the intermediate steps.
You data kind of look like the result of the “null distribution” algorithms, where no fibre orientation information is used and streamlines just select orientations entirely at random, which is what would happen if (for some reason) your ODFs were spherical with no information beyond l=0.
I also note that you’re using
mtnormalise with only a single tissue as input. I don’t know from experience exactly what effect this will have, but it does intuitively defeat the purpose of a “multi-tissue”-based approach. If you have both b=0 and b>0 data you can do a two-tissue WM+CSF deconvolution; don’t know that that’s the source of your reported issue, but it’s worth considering its use anyway.