Yep, not surprising they’d look bad in that setting. Spatial resolution shouldn’t be the main problem, but intensity normalisation certainly will be: the registration uses an SSD metric. The same CSD method is not absolutely crucial, but to e.g. apply mtnormalise you’ll at least need a 2-tissue (or more) CSD variant. Just for the sake of registration, I also wouldn’t worry too much about the response function(s) matching up exactly. I’ve some experience on that front, testing a few things: no problem there. What I would suggest with the current tools is to try and run a 2-tissue CSD (WM-CSF, with
dwi2response dhollander responses), and apply
mtnormalise to the 2-tissue result. Additionally, also apply
mtnormalise to the HCP data template, but using its full 3 tissue types. I suppose you only have a WM FOD volume for that template though… I’m hoping you still have the warps for all HCP (WM FOD) images to template space, as well as their individual GM and CSF volumes…? If so, apply the same warps to the GM and CSF volumes, and average their results, so you’ve got a full “3-tissue template”. Those 3 (template) tissues then feed into
mtnormalise. Once your 3-tissue template and 2-tissue subject have gone through
mtnormalise, they should register far better. Some details within the cortical GM might not align perfectly, but that’s not a concern if you’re mostly interested in WM alignment.
Better still would be to have put the HCP subjects 3-tissue CSD results through
mtnormalise individually, before creating a template from them (maybe you have effectively done that?). Then you don’t even have to be bothered with warping the GM and CSF volumes (as their only purpose in this particular story is to inform
mtnormalise). And then again put the 2-tissue result from the subject through
mtnormalise of course as well (just as above).
As mentioned, I have been able to pull of good results with similar strategies in other scenarios. The final chances of success still depend on the x-tissue CSD result of the individual subject. 3-tissue will work better here, since the template is (derived from) 3-tissue too, but as mentioned, I’ve seen it work too with 2-tissue to 3-tissue (template).