we are using DARTEL (in SPM5, and by the way, fascinated by the
improvement it brings in terms of registration), and I have two more
(1) "DARTEL space" vs. "absolute" MNI space:
I noted that the 6th template is systematically shifted in some areas we
are very interested in (e. g . the hippocampus comes to lie 4 mm more
superior than the original templates and the colin MNI single brain) - it
brings a bit of a problem when using MSU or other localisation tools that
assume results are close to MNI space.
In an earlier analysis we found that the normal SPM5 segmentate function
results came to lie close(er) to MNI space - what could be done to get a
better matching here?
(2) If 1 mm3 resolution is desired (in the end), I guess it needs to run
via 1 x 1 x 1 mm3 iterative template creation? Or is there a step (similar
to the VBM toolbox write segmentate) that would write out 1x1x1 mm3 from
the lower (1.5 x 1.5 x 1.5 mm3) flow fields?
(3) Smoothing: can one read from the resel size how much smoothing is
appropriate? We stepped down from 12 mm to 8 mm with DARTEL, which wrt
some aspects seems "ok", however, is there an objective way to get out of
the arbitrariness in choosing a kernel?
Thank you VERY much on any hints on this,