Dear John,

does this mean that for a common VBM-Dartel approach the data could be imported as 
affine transformed segmentations and only for shape analysis approaches (e.g. 
classification?) the data should be rigidly aligned?

Best regards,

Christian

On Mon, 26 Jan 2009 13:59:49 +0100, John Ashburner <[log in to unmask]> 
wrote:

>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: [log in to unmask]
>> http://dbm.neuro.uni-jena.de