Dear Krish;

We had used the third method, but have changed to the first one.

As you mentioned, the method using the EPI template will correct
the distortion of the EPI images. However, I once observed that some
region traveled too much. In that case, parietal activation was consistently
seen among subjects, but one subject's activation located in the occipital
lobe. Re-normalising with T1 template-method ameliorated the problem.

The EPI-template normalisation works very well for the whole brain
shape or the contour, but may not for inside parenchyma. This can be
checked if you normalise the T1 images using the normlisation parameters
estimated by using the EPI template.

In conrast, the T1-template normalisation looks more conservative. Although it
will leave the distortion, normalisation is sane. The parietal lobe never run
into the occipital lobe!

Of course, it depends on the degree of distortion of your EPI image.
The distortion is not so big in our MRI scanners. I always check it
after corregistering T1 and EPI via the "check correg" button.


At 9:42 AM +0000 0.3.8, Krish wrote:
>Hi,
>
>I have a question about how one should normalise EPI fMRI datasets.
>There seems to be (at least) three possible approaches, and I wonder if
>there is a consensus on which is best (or least worst....)
>
>1) Take a set of high-resolution T1 weighted slices in the same plane
>and with the same FOV as the EPI slices. Normalise this to the T1
>template and then apply the normalisation parameters to each EPI volume.
>
>2) Take a high-resolution multi-shot EPI image, again with exactly the
>same parameters as the single -shot functional EPIs, normalise this to
>the EPI Template and then apply the normalisation parameters to the
>single-shot functional EPIs
>
>3) Simply normalise the single-shot functional EPI (usually the mean
>image from the realignment) to the EPI template.
>
>In spm99b, option (3) appears to work really well, so long as the
>spm_defaults are modified so that the BrainOnly option is disabled, and
>it has the advantage of not requiring any other acquistions. It also has
>the advantage that the spatial distortions inherent in single shot GE
>EPI are corrected for. However, there is an assumption here that those
>distortions *should* be corrected. Surely there are some artefacts on
>such images which are dropouts and so that particular region should not
>be pulled out to match the edge of the template.
>
>We have done a quick comparison of (1) and (3) on two of our group
>studies. On one of our studies we obtained more significant activations
>in the final statistical analysis if we used option (3) (i.e.
>normalising the single shot EPIs directly) compared to (1). This was
>initially a surprise as we naively expected only the location of the
>clusters to change. We then came up with the idea that probably option
>(3) was bringing the group data into better register with each other -
>hence the better statistics.
>
>Of course, it had to happen, in the other study we looked at, Option (1)
>gave better final statistical results than Option (3).
>
>Any thoughts? I'd be really interested to know what other groups are
>doing, and whether anybody has done a more rigorous comparison of the
>various methods.
>
>Thanks in advance,
>
>krish
>
>
>--
>Dr Krish Singh, ([log in to unmask])
>Magnetic Resonance and Image Analysis Research Centre, Liverpool
>University
>Pembroke Place, Liverpool, L69 3BX, UK. Tel 0151 7945645. Fax 0151 794
>5635


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