Thanks for relpy.
I did try the exact approach as Philip described. However, the output
image, which should be in native space, still has the same dimension as
the input normalized image and only shows part of the brain where the
edges were cut.
Should the codes by marko do the same thing as the approach described? I
tried it too. It did produce the image in the native space, but
strangely, one slice bigger (the native space has the dimension of
256x256x176, the output image is 256x256x177). Why does this happen?
Many thanks
Dr Shan Shen
Cognitive Neuroscience Research Team
Department of Psychology
University of Surrey
Guildford GU2 7XH
UK
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
On Behalf Of Saemann Philipp
Sent: 19 January 2007 16:09
To: [log in to unmask]
Subject: Re: [SPM] inverse normalization in SPM5
Hello Shan,
I understand that you want to apply the inverse matrix to a normalised
image and by this
obtain an image in native space. For this you simply use "normalise
write",
pick
the _seg_inv_sn.mat file and an image and run it. This will apply the
already
inverted matrix to that image. The voxel size of the image to be written
and the interpolation
type may also be changed.
Maybe you could specify what exactly has not worked with the approach
described,
best regards,
Philipp
Max Planck Institute of Psychiatry
NMR Research Group
Kraepelinsr. 2-10
80804 Munich
Mail: [log in to unmask]
Phone: 0049-89-30622-413
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