  We study the performance of CLAIRE -- a diffeomorphic multi-node, multi-GPU
image-registration algorithm, and software -- in large-scale biomedical imaging
applications with billions of voxels. At such resolutions, most existing
software packages for diffeomorphic image registration are prohibitively
expensive. As a result, practitioners first significantly downsample the
original images and then register them using existing tools. Our main
contribution is an extensive analysis of the impact of downsampling on
registration performance. We study this impact by comparing full-resolution
registrations obtained with CLAIRE to lower-resolution registrations for
synthetic and real-world imaging datasets. Our results suggest that
registration at full resolution can yield a superior registration quality --
but not always. For example, downsampling a synthetic image from $1024^3$ to
$256^3$ decreases the Dice coefficient from 92% to 79%. However, the
differences are less pronounced for noisy or low-contrast high-resolution
images. CLAIRE allows us not only to register images of clinically relevant
size in a few seconds but also to register images at unprecedented resolution
in a reasonable time. The highest resolution considered is CLARITY images of
size $2816\times3016\times1162$. To the best of our knowledge, this is the
first study on image registration quality at such resolutions.
