Thanks for the advide!
Unfortunately my scan not only consists of just 20 slices, these slices
are of bad quality due to some motion of the patient. Therefore the
segmentation of gray/white/CSF was not very successful.
I therefore have two questions:
1. What can be done to increase the quality of the segmentation under such
conditions? And, once i've done the segmentation, how do I use it to
normalize my images?
2. Is there any other way to get a acceptable normalization under such
conditions?
t.i.a.
Johannes
________________________________
Von: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] Im
Auftrag von Sung Lai Yuen
Gesendet: Donnerstag, 3. November 2005 08:35
An: [log in to unmask]
Betreff: Re: [SPM] Unexpected tilt after normalization
Dear Johannes,
I've been dealing with a similar case and asked for help from list before
-- I had to normalize a patient's brain with massive frontal & parietal
lesions. The advice I received from John is to segment the brain and just
normalize the segmented grey matter image wrt the grey matter apriori
template in SPM. I tried this approach and it greatly improves the
normalization quality. Hope this helps.
Cheers,
Kenneth Yuen
________________________________
Von: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] Im
Auftrag von Johannes Rüter
An: [log in to unmask]
Betreff: [SPM] Unexpected tilt after normalization
Dear SPM Users,
I'm pretty new to SPM and have encountered a strange error while trying to
normalize my data.
I have data from a single subject with a very large bilateral medial
occipito-temporal lesion. Unfortunately this whole brain scan consist of
only twenty transversal T2-weighted slices. After converting my images
into analyze images using MRIcro, I designate the large regions of
interest, export them into analyze format as well and feeds everything
into SPM2.
Doing the normalization step by step, applying a basically default
normalization (linear transformation only, 2nd degree B-spline
interpolation, a slightly larger bounding box), the result is quite good
at first glance. Taking a closer look it becomes obvious, that on the one
hand the head has been clockwisely tilted by nearly 45 degree, and one the
other hand that the ROI distribution has changed significant (e.g. into
the cerebellum), probably a result of the bad fit. Orienting the source
image onto the bicommissural line does not help, nor does tilting the
source image counterclockwise from there.
Which parameters should I check (and/or change) to get rid of the tilt in
the normalization process and thereby getting a better fit to the
template? Are there any methods or parameters to deal with these extended
lesions?
Thanks in advance for the advice
Johannes
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