Nonrigid image registration is an important, but time-consuming task
in medical image analysis. In typical neuroimaging studies, multiple
image registrations are performed, i.e. for atlas-based segmentation
or template construction. Faster image registration routines would
therefore be beneficial.

In this paper we explore acceleration of the image registration
package elastix by a combination of several techniques: i)
parallelization on the CPU, to speed up the cost function derivative
calculation; ii) parallelization on the GPU building on and
extending the OpenCL framework from ITKv4, to speed up the Gaussian
pyramid computation and the image resampling step; iii) exploitation
of certain properties of the B-spline transformation model; iv)
further software optimizations.

The accelerated registration tool is employed in a study on
diagnostic classification of Alzheimer's disease and cognitively
normal controls based on T1-weighted MRI. We selected 299
participants from the publicly available Alzheimer's Disease
Neuroimaging Initiative database. Classification is performed with a
support vector machine based on gray matter volumes as a marker for
atrophy. We evaluated two types of strategies (voxel-wise and
region-wise) that heavily rely on nonrigid image registration.

Parallelization and optimization resulted in an acceleration factor
of 4-5x on an 8-core machine. Using OpenCL a speedup factor of ~2
was realized for computation of the Gaussian pyramids, and 15-60 for
the resampling step, for larger images. The voxel-wise and the
region-wise classification methods had an area under the
receiver operator characteristic curve of 88% and 90%,
respectively, both for standard and accelerated registration.

We conclude that the image registration package elastix was
substantially accelerated, with nearly identical results to the
non-optimized version. The new functionality will become available
in the next release of elastix as open source under the BSD license.