A torus would be cool.
No, just like with human data, you want to see increasingly sharper “disk” shapes for higher b-values, at least for the WM response. You can open it in
shview and use left/right arrow buttons to go through each b-value. For b=0 it’ll be a perfect sphere (by definition).
More importantly, you’ll want to check the voxels selected to see they make sense for each tissue type (WM GM CSF). That’s possible by adding the
-voxels myvoxels.mif option to
dwi2response dhollander when you run it. And then view those voxels by overlaying them with e.g. your dMRI data (e.g. a b=0 image) for reference. If the voxels selected aren’t sensible, this most likely is corrected by setting a good value via the
-fa option to the
dwi2response dhollander algorithm. By default, it’s set to 0.2, which works fine for a wide range of applications, mostly human ones. Depending on your b-values, whether it’s in vivo or ex vivo, etc… you sometimes might want to consider lowering that, to e.g. 0.15 or even lower. That value needs to be set to an FA value that roughly separates WM and GM for your data. It doesn’t have to be perfect (the rest of the algorithm fixes everything for you), but at least roughly ok-ish.
And in general, always look at images (and show them to us!). From your commands, it’s hard to tell what is terrible about the results, or at which step it became terrible (and once something becomes terrible, it’s trash-in trash-out for all subsequent steps; so we need to identify where things are going wrong).
It’s definitely possible to get good results for challenging animal data; we were recently able to apply these techniques even to growth restricted newborn lambs. Note that, for that to be a success, we had to rely on the new 2019 version of the
dwi2response dhollander algorithm (also talk here). In the current RC3 release of MRtrix3, still the older 2016 version is used. The newer 2019 version is already released here. It might also possibly make it into the next MRtrix3 release, but I can’t guarantee that (if or when) personally. Let’s hope so.
Other than that, do show us those selected voxels overlaid with e.g. a b=0 image for reference. Also maybe show us all outputs from MSMT-CSD.
Another tip: convert our data to .mif format right at the start of the pipeline, i.e. when importing the data, so you don’t have to work with .nii.gz and bvals and bvecs throughout. Too many opportunities for things to go wrong there; simply avoid.
Some of your commands to look strange though (and can’t even work): you need to get 3 tissue responses from
dwi2response dhollander, and use 3 pairs of responses and FODs/compartments with MSMT-CSD. As you wrote your commands here, they would’ve error’ed out for sure.
And yet another tip: importing Bruker data is a nightmare; so first check e.g. with a simple DTI fit and FA and MD maps whether importing worked fine.