Hi Mrtrix experts,

I analyzed my data in diffusion space, and did whole brain tractography. Now I would like to transform my tracts to standard space. I thought I could use the tcknormalise command for this purpose, so using FSL flirt I estimated the transformation from DWI to T1 and then to MNI space. However, if I do “tcknormalise tracks_in.tck dwi2standard.nii.gz tracks_out.tck”, what I obtain is one big string of tracts, on the outside of my brain.
Did anyone of you already use this command or could somebody explain me what I’m doing wrong?

Thank you!

Kind regards,

This isn’t something I’ve worked with myself, but since nobody else has jumped on yet:

tcknormalize expects to be able to read a triplet of values in the image underlying a particular point, and these values correspond to the new position of that point. Therefore if your transformation software provides spatial offsets, these won’t be interpreted correctly.

From memory the secret is the warpinit command: This provides a ‘unit warp’ where the value in each voxel is the real-space position of that voxel itself; therefore the transformation performed as described above would have no effect. But once you add the calculated non-linear displacement field to this, you get the correct behaviour.

Anyone who has actually used these features, feel free to chime in. The documentation’s a little light on this stuff, probably because once our own registration is up and working, handling these kind of tasks will change.

Yes, sorry, this is something that I said I’d document a while back, but I still haven’t got round to it… You’ll find this post probably covers what you need to know.

Hi !
I have a simple question.I don’t know how to use tcknormalise.
Similar to @haerts does, I use the FSL flirt transform the native FA to native T1 getting a FA2T1.mat and FA2T1.nii, then transform the native T1 to standard MNI152 getting a warp.nii and T12MNI.nii .
Next, because I have a FA2T1.mat and T12MNI_warp.nii ,I don’t what to do …Could you give some advises? @haerts @rsmith @jdtournier

Liyuan yang

Have you looked at this more recent post? It’s a bit more detailed, and should hopefully clarify the process…