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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