Thanks for the reply. We did separate realignment(realign to the 1st image in the session) for multiple sessions here. Our task may introduce some movement between sessions so we prefer do realignment separately then create session-specific mean image then coregister these mean images to T1 (from there we do segmentation to get the .mat assuming T1 stationary) So can you clarify that we should do normalization separately even we use the same parameter?
Thanks so much,
From: John Ashburner [mailto:[log in to unmask]]
Sent: Wednesday, December 22, 2010 3:51 AM
To: Jiang, Zhiguo
Cc: [log in to unmask]
Subject: Re: [SPM] Normalise:Write issue
I am guessing that you refer to the masking that is done. To avoid
some motion artifacts in the fMRI, only those voxels that correspond
to a point within the FOV of all of the original images are used. If
this is not the case, then that voxel is set to zero in all scans.
On 20 December 2010 17:29, Jiang, Zhiguo <[log in to unmask]> wrote:
> Dear SPM community:
> Recently I ran into an annoying issue with Normalise:Write in SPM8. (Not
> sure about SPM5). Say,I have an estimate of the normalization(either from
> Normalise:Estimate or Segmentation of T1), then I apply the normalization to
> a series of images, ). Lets say, you have a subject whom you collected three
> runs of scans. In batch interface, you run Normalise:Write by selecting all
> functional images at once vs. using three modules each consisting one run.
> Leave all other parameters the same, you are expected to see same normalized
> images. The result is really annoying. You get slight different images. Not
> sure what causes the problem.
> To sum it up , Normalise:Write will generate different results depending on
> how you select the images (multisessions). If you select the multi sessions
> in one module , your results will be different from if you use multilple
> modules. Can someone point out what might be causing the problem? How I
> should select the images? I really have no idea of which results I should
> Thanks a lot,
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