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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