  We introduce an ultrahigh-resolution (50\mu m\) robotic micro-CT design for
localized imaging of carotid plaques using robotic arms, cutting-edge detector,
and machine learning technologies. To combat geometric error-induced artifacts
in interior CT scans, we propose a data-driven geometry estimation method that
maximizes the consistency between projection data and the reprojection
counterparts of a reconstructed volume. Particularly, we use a normalized cross
correlation metric to overcome the projection truncation effect. Our approach
is validated on a robotic CT scan of a sacrificed mouse and a micro-CT phantom
scan, both producing sharper images with finer details than that prior
correction.
