Dear list members,
I'd like to coregister two high resolution (0.75 mm cubed) MRI brain
datasets of different modalities to one another using spm5. However, the
coregistration keeps matching the non-brain material (fat rim of the skull)
instead of the brain. Therefore I'd like to use a weighting volume as in
the realignment approach. Because I couldn't figure out where and how to do
this, I decided to try to coregister after masking the images. (These masks
I would create by performing skull stripping on each set, so keep in mind
that they're likely to differ a bit.)
I'm affraid however that the spm_coreg (normalized mutual information)
routine tries to fit the masks instead of the DATA within the masks. A test
using a simple test volume seems to show that the routine indeed suffers
from the amount of zeros outside the ROIs of both images. (I think because
the zeros have of course a lot in common, (a large peak in the lower corner
of the joint histogram), forcing the masks on top of eachother with such
strength that the information within the masks simply cannot overcome.)
So I thought I could remove the influence of the data outside the mask in
the joint histogram, by replacing the zeros in the data by noise, uniformly
distributed over the dataset's entire intensity range. Instead of the data
outside the mask showing up as a peak in the joint histogram I expect it to
be uniformly distributed over the entire histogram now. This is sadly not
the case:
If you make a joint histogram of two uniformly distributed noise datasets,
the histogram looks like a 2D gaussian. I did not expect this at all. Is
this an error/feature of spm_hist2 or was I expecting totally the wrong
output in the first place and should it be gaussian after all?
%%% example, this will show a 2D gaussian distribution:
x = uint8(255*rand(50,50,50));
y = uint8(255*rand(50,50,50));
h = spm_hist2(x,y,eye(4),[1 1 1]);
figure;imagesc(h);
%%%
To summarize:
A) is there a way to coregister the datasets using a weighting volume?
B) if not, how can I make spm_coreg ignore the data outside the ROI
properly?
C) is the gaussian output of spm_hist2 a bug or feature (caused by, for
instance, the interpolation step in there) or is the joint distribution of
two noise datasets supposed to be gaussian after all?
Thanks for any help in solving this,
Regards,
Peter Koopmans
Phd student MR
FC Donders Centre for Cognitive Neuroimaging
Nijmegen, The Netherlands
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