Hi all,
in general, I would agree with Jonathan.
On some occasions I actually did use a mean EPI from high-resolution
(2x2x2 mm^3) fMRI data at 3T for 'unified segmentation'. It did result
in GM and WM probability maps that looked good enough for 9 out of 10
subjects, and hence I used the ensuing normalization parameters for the
EPIs. I only got this working on high resolution EPI as there was quite
usuable anatomical contrast in them, I indeed think at low resolutions
EPI (eg 4x4x4 mm^3) this shouldn't be attempted.
For some acquisition protocols (as these hi-res EPIs) registration to T1
doesnt work well because of the spatial distortions that are endemic to
EPI. We in that case got better results doing 'unified segmentation' on
the mean EPIs directly.
Cheers,
Bas
Op 08-01-10 13:33, Jonathan Peelle schreef:
> hi Maaike
>
>
>> I hope you can help me out with the following issue. There is some debate going on within our group which scan we should use in the Data field to produce the normarisation parameters by segmentation. In the examples in described in the manual, the structural image is used, but some say we should use the mean epi created in during the realign& unwarp step.
>>
> If you are doing normalization by segmentation, it's the structural
> image you want to enter into the segmentation routine. Before doing
> so, you'll want to coregister the structural to the mean EPI
> (post-realignment), which ensures that your structural image is in the
> same space as your functional images (and thus that the normalization
> parameters estimated from the structural image can be applied to the
> functional images as well).
>
> When you cogegister, the mean EPI is the reference image (remains
> stationary), and the structural image is the source image (moved about
> to match the mean EPI).
>
> Hope this helps,
> Jonathan
>
--
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Dr. S.F.W. Neggers
Division of Brain Research
Rudolf Magnus Institute for Neuroscience
Utrecht University Medical Center
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