I would be more inclined to DARTEL all three datasets together, than do them
seperately and try to join the data together afterwards.
Also, make sure you use the latest updates as there was a fix made to the
template generation part.
Best regards,
-John
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Dear John,
we have three large (each >200) high resolution T1 datasets that should
ideally be joined
for a large VBM analysis. The differ slightly in voxel size and contras and
low frequency
intensity bias; each of them is (for itself) of sufficient quality for VBM.
For now, I
DARTELed all three datasets separately, planning to introduce a covariate for
the three
different datasets later to correct for systemic differences between the three
raw data
types.
I have two questions:
1. Would you (after all three datasets were brought to MNI space by affine
normalisation
of the respective 6th gen. template with MNI template) still do some
adjustment/coregistration between the three templates?
2. Would joining all datasets irrespective of their different raw data
features in one
DARTEL process be okay? I guess systemic differences of the resulting
modulated images
could still be there but corrected in the model then. It is more to optimize
cortical
alignment. However, I am not sure if e. g. the different bias are better
corrected if the
datasets are handled separately first.
Thanks a lot for any comments/support!
best regards,
Philipp
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