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John, Is the "Import DARTEL" step using 12 parameters or 6 parameters? On Mon, Jan 26, 2009 at 8:17 AM, Christian Gaser < [log in to unmask]> wrote: > 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 > -- Best Regards, Donald McLaren ===================== D.G. McLaren University of Wisconsin - Madison Neuroscience Training Program Office: (608) 265-9672 Lab: (608) 256-1901 ext 12914 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (608) 265-9672 or email.