Dura is a pain. If I had my way, no human brain would have any. Well, OK, that might have health consequences, so maybe not that—back to the drawing board…
If the goal of this is alignment of EPIs, which typically have much lower resolution, this level of mis-detail seems like it should be OK. The actual, individual human brain can be squished and squashed a bit differently than templates (often an amalgamation of subjects) assume or allow. It makes it tricky to get every part of the brain 100% right when adjusting parameters (i.e., getting these parts pulled a bit more tightly over the template might produce a problem somewhere else).
But, that being said, if you have some extra+unwanted free time, then these are a couple of things I could think of to try by adding options to your @SSwarper call (though, again, they really might not be necessary):
- adjust the final round’s cost function to LPA (default is PCL):
-cost_nl_final lpa
- try running the NL alignment to a higher level of refinement:
-minp 8
This sets the “-minpatch …” value for 3dQwarp (which is the actual program called under the hood by @SSwarper). The default is ‘11’, which sets the size scale of the final patch refinement. Setting it to, say, 8 or 9 might produce a bit of change. The minimal value is 5. NB: lowering this value will slooooooooow the program down.
Of the 2 options above, I would try the first one (-cost_nl_final lpa) first. That might increase processing time a wee bit, but probably not meaningfully. Asking for much more detailed patch refinement will noticeably extend processing time.
As with all nonlinear alignment program usage in AFNI, I hope you are leveraging any multiple CPU usage (because they are inherently parallelized with OpenMP):
afni_system_check.py -check_all | grep "number of CPUs:"
… and check how many AFNI will use at that moment:
afni_check_omp
–pt