Sear Stephen,
You might want to try the High-dimensional warping toolbox for this.
Eventually I plan to modify DARTEL to make it suitable for serial scans, but
that work won't be ready for a while. For the moment the HDW toolbox is the
best I can offer for this kind of thing.
Best regards,
-John
On Tuesday 18 November 2008 21:47, Stephen J. Fromm wrote:
> On Tue, 18 Nov 2008 19:26:51 +0100, John Ashburner
>
> <[log in to unmask]> wrote:
> >> Is there any reason to avoid using the "affine only" setting in the
> >> spatial normalization module? I would have thought that would give an
> >> affine transform that's more general (flexible) than rigid body.
> >
> >It is more flexible, but this may not really be what you want. Note also
> > that by default it regularises the registration, which has a slight
> > biasing tendancy to zoom up the images (because the MNI brains are bigger
> > than real brains). This can be changed via the UI though.
>
> (Thanks for your help.)
>
> Oh, OK...I assume you're referring to the Bayesian framework you use?
>
> >> Two more questions on within-subject registration in longitudinal VBM:
> >> * Is there any reason to prefer rigid body over affine?
> >
> >The best model depends on whether or not you expect the voxel sizes to be
> >correct etc. If the head moves as a rigid-body, then rigid should be the
> >better model, as the zooms and shears of the affine should be known. If
> > they are known, then estimating them will only introduce errors. However,
> > if there are issues with incorrect voxel sizes in the headers etc, then
> > an affine model may be preferable. If this is the case, because VBM is
> > actually trying to compare volumes, your data may need to be caligrated
> > in some way.
>
> In this experiment, the subjects are rhesus monkeys, and going from
> juvenile to adult their brains are growing a little bit. So the precise
> context is coregistering within subject, but at different developmental
> timepoints.
>
> Best,
>
> S
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