>I wanted to be able to model the shape and size of the brain from the
>deformations, and not just some measure that has an affine transform factored
>out from it. In practice though, because DARTEL uses a constant velocity
>framework, it means that the further it has to deform something, the less
>accurate are the resulting warps. Beginning with an affine reg may therefore
>result in more accurate inter-subject alignment, but the shape measures would
>be a mess to work with. Variable velocity (eg LDDMM) or geodesic shooting
>methods have fewer such issues - but they are quite a lot slower.
>
>My principle aim was not to spatially normalise the brains to MNI or Talairach
>space. I primarily wanted the brains to be aligned to the other brains in
>the study, with as little bias as possible. Shape is nonlinear, so if linear
>approximations are to be used for studying it, then it is more accurate to
>base those linear approximations at some point close to the average shape.
>See eg
>
>Statistics on diffeomorphisms via tangent space representations
>NeuroImage, Volume 23, Supplement 1, 2004, Pages S161-S169
>M. Vaillant, M.I. Miller, L. Younes, A. Trouvé
>
>I figured that I would eventually get around to coding up a second step to put
>all the brains into MNI space and therefore make it easier to use DARTEL for
>more general spatial normalisation purposes.
>
>Best regards,
>-John
>
>On Monday 26 January 2009 10:49, Christian Gaser wrote:
>> Dear John,
>>
>> Dartel is initially using rigidly aligned segmentations. That means that no
>> size scaling of the segmentations is considered before estimating the
>> non-linear warps. Although the underlying diffeomorphic registration method
>> can cope with large deformations, I am wondering why it is not more
>> appropriate to use affine transformed images (maybe restricted to 9
>> parameters). The varying scalings of the images might introduce unnecessary
>> variance/noise. An additional scaling step before warping might result in
>> much smaller deformations which are needed to register the brain to the
>> template. Furthermore, if the template is in MNI space, the resulting
>> images will be to and the postprocessing step of aligning the images to MNI
>> space could be probably skipped. What is the advantage of using rigid body
>> transformation rather than affine transformation?
>>
>> Best regards,
>>
>> Christian
>>
>>
___________________________________________________________________________
>>_
>>
>> Christian Gaser, Ph.D.
>> Assistant Professor of Computational Neuroscience
>> Department of Psychiatry
>> Friedrich-Schiller-University of Jena
>> Jahnstrasse 3, D-07743 Jena, Germany
>> Tel: ++49-3641-934752 Fax: ++49-3641-934755
>> e-mail:
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>>
http://dbm.neuro.uni-jena.de