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?
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]