What is MNI_ANIMAL?

One of the main sources of difficulty when comparing anatomical data from a number of subjects is anatomical variability. Even after automatic linear registration [1], there remains a residual non-linear component of spatial mis-registration among brains that is largely due to normal anatomical morphometric variability that can account for a difference in position of up to 1-2cm for the same anatomical landmark between subjects.

We have developed a completely automatic method, based on multi-scale, three dimensional (3D) cross-correlation, to non-linearly register two MRI volumes together, thus reducing the residual mis-registration remaining after linear alignment.

Spatial registration is completed automatically as a two step process. The first [1] accounts for the linear part of the transformation by using correlation between Gaussian-blurred features extracted from both volumes. In the second step, ANIMAL estimates the 3D deformation field [2] required to account for this variability. The deformation field is built by sequentially stepping through the target volume in a 3D grid pattern. At each grid-node i, the deformation vector required to achieve local registration between the two volumes is found by optimization of 3 translational parameters (tx_i,ty_i,tz_i) that maximize the objective function evaluated only in the neighbourhood region surrounding the node. The algorithm is applied iteratively in a multi-scale hierarchy, so that image blurring and grid size are reduced after each iteration, thus refining the fit.

A Perl script (nlfit) implements the multi-resolution fitting strategy that has been used to map more than 600 brains into stereotaxic space at the Montreal Neurological Institute. At the heart of this procedure is a new version of minctracc, the program that automatically finds the best non-linear transformation to map one volumetric data set (stored in MINC format) on to another. The program uses optimization over a user selectable number of parameters to identify the best transformation mapping voxel values of the first data set into the second.