Dear SPMer'S, dear Alle Meije's, dear Matthew
Sorry for the delay in replying to the several proposals concerning my
problem:
> When normalising PET data not to the template, but to the mean-Image,
> that is created
> in the first realignment-step, i.e. doing a transformation of this
> mean-Image on itself, the
> results are really poor.
> The reason for doing this kind of normalisation is to analyse individual
> volumes of the activated/deactivated clusters in specific regions.
> Has anyone an idea, why the results of the normalisation step are not
> very satisfying?
The explain the aim more precisely: I want to correlate data from
Doppler-Sonography (I d'ont know if that is the right terminus in
english, I hope so ?) with the activations/deactivations from patients
with shizophrenia obtained by PET and SPM99 while
doing several tasks. In detail the parameters of interest are the
volumes and the z-values
of clusters belonging to special regions in the brain. I thought of
doing an individual
normalization to conserve the voluminae of the (de)activated clusters as
given in the spm
table. Normalisation is not necessary in this case, realignment and
smoothing would work too, but I started that way and now I am wondering,
why the normalisation isn't working very well. I hope that explains a
little more.
Now I started with the proposals of Alle Meije and Matthew:
Smooting the mean-image with 8mm, disabling the template brain masking
but the results are the same. Maybe I really need to have a look at the
global vars...
Some other idea's from the community ????
Thank you very much in advice
Best,
Laszlo
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