5tt file, mouse brain, tracts detected only in the subcortical gray matter

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Hi Francesca,

Okay, a lot to get through here…
buckles seat belt
These may not come out in any particular order, so just bear with me.

  • ACT with EPI distortions: From the signal pileup (bright strips) I see, it looks like you don’t have the necessary data for performing EPI susceptibility distortion correction. This is generally considered a prerequisite for applying ACT, since otherwise you cannot guarantee that image data from the same anatomy is being accessed from both images for any given point in space. I don’t know how isolated these inhomogeneities are for in vivo mouse imaging, it might only be a localised effect, but it’s something to consider nonetheless.

  • dwi2response version: If the command you have quoted is precisely what you specified at the command line, then it looks like you’re using an out-of-date MRtrix3 installation. In the current version, dwi2response is a script rather than a binary command, with slightly different usage. More importantly, the recommended algorithm differs from that used in the older binary version, since the latter was observed to misbehave in a number of scenarios. So I would strongly recommend updating your MRtrix3 and re-trying your processing pipeline from the very beginning.

  • bvecs / bvals import: Eyeballing your tracking data in the absence of much detailed mouse brain anatomical knowledge, it’s possible that the gradient table imported from these files is being interpreted incorrectly. This can most certainly occur if your MRtrix3 version is earlier than 0.3.14, which I believe is the case given the prior point regarding dwi2response. If you have access to the raw data from the scanner, you can try converting directly to .mif rather than going first to .nii / bvecs / bvals and then to .mif. But even if that works I would update your MRtrix3 installation anyway :smiley:

The csd file is composed of 28 volumes, and if I well understand, this means that I have 28 different spherical harmonic components, is it reasonable?

Yes, that’s correct: 28 volumes corresponds to lmax=6. All but the first volume don’t make much sense to visualise as scalar maps; it’s better to use the ODF tool in mrview to visualise these data. This would also give more clues as to whether or not the diffusion gradient table is a problem: if it is, the ODF glyphs won’t align properly with the expected fibre directions.

I built by myself the 5TT file (that you can find attached here) and I registered it in the diffusion space (also if I think that it is not strictly necessary).

If your animal is secured stereotactically, and your scanner calibrates its resonance frequency properly, then there’s a good chance that there won’t be any appreciable translations or rotations between DWI and anatomical scans. You would however want to be confident that this is indeed the case before omitting an explicit registration step from your processing chain.

  • Using ACT in mouse brain: Rather than trying to precisely duplicate the HCP pipeline, I would more carefully consider what methods are appropriate to use given the data you are dealing with. Probably the primary strength of ACT is constraining streamline propagation to the white matter (with the exception of limited propagation into sub-cortical grey matter structures); however this is based on human data, where the white matter is large in volume, sub-cortical grey matter structures are comparatively small, and terminating streamlines precisely at the interface between cortical grey matter and white matter gives accurate information about which area of the cortex that streamline should be associated with. In your data, the white matter is extremely thin, so streamlines are unlikely to make it very far before they exit the white matter. Also, there are no streamlines in the cortical GM since they are terminated as soon as they enter it; but there exists significant oriented structure within this tissue that can be tracked.
    I would try two things here:
    • Add your ‘cortical GM’ segmentation to the ‘sub-cortical GM’ volume, and use an empty volume for the ‘cortical GM’ component of the 5TT image. This will allow streamlines to propagate into this tissue.
      (Note: This also works for allowing tracking into the cortex for human images…)
    • Simply try tracking without ACT. You may find that for your data, it introduces more problems than it solves. You could still use the CSF volume as an exclusion mask for tracking.

It seems that the algorithm detects tracts only in volume 1 of the 5tt file, where am I wrong?

You should find that streamlines overlap with volumes 1 and 2 of your 5TT image. This is precisely what ACT is designed to do: Permit streamlines propagation only within white matter, and into sub-cortical white matter structures. The problem is probably that the behaviour of ACT, which was designed with human data in mind, contradicts your expectations of what should be reconstructed in a mouse brain.

The problem is solved when I use as seed image for tckgen the dwi mask (as described in your documentation in the tutorial Basic DWI processing)

I think you’ll find that the key difference in these results is not the difference in streamline seeding, but that you have not used ACT.

wipes brow

Phew, wasn’t that fun?!

I would start with an MRtrix3 update and some basic whole-brain non-ACT tracking to make sure that your gradient table is correct and the FODs are pointing in the right directions. From there you can start experimenting with different aspects of processing / reconstruction to see what works for you.

Cheers
Rob

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