This toolbox is based around the “A Fast Diffeomorphic Registration Algorithm” paper
(Ashburner, 2007). The idea is to register images by computing a “flow field” which can
then be “exponentiated” to generate both forward and backward deformations.
Processing begins with the “import” step. This involves taking the parameter files
produced by the segmentation, and writing out rigidly transformed versions of the tissue
class images, such that they are in as close alignment as possible with the tissue
probability maps.
 
The next step is the registration itself. This involves the simultaneous registration of e.g.
GM with GM, WM with WM and 1-(GM+WM) with 1-(GM+WM) (when needed, the 1-
(GM+WM) class is generated implicitly, so there is no need to include this class
yourself). This procedure begins by creating a mean of all the images, which is used as
an initial template. Deformations from this template to each of the individual images are
computed, and the template is then re-generated by applying the inverses of the
deformations to the images and averaging. This procedure is repeated a number of times.
 
Finally, warped versions of the images (or other images that are in alignment with them)
can be generated.
 
This toolbox is not yet seamlessly integrated into the SPM package. Eventually, the plan
is to use many of the ideas here as the default strategy for spatial normalisation. The
toolbox may change with future updates.
 
There will also be a number of other (as yet unspecified) extensions, which may include a
variable velocity version. Note that the Fast Diffeomorphism paper only describes a sum
of squares objective function. The multinomial objective function is an extension, based
on a more appropriate model for aligning binary data to a template (manuscript under
review).

J. Ashburner. A Fast Diffeomorphic Image Registration Algorithm. NeuroImage, 38(1):95-113, 2007.
 
These descriptions of the new features are taken from the SPM8 Release Notes
 
BACK