DATA-DRIVEN IMAGING GEOMETRIC RECOVERY OF ULTRAHIGH RESOLUTION ROBOTIC MICRO-CT FOR IN-VIVO AND OTHER APPLICATIONS.

We introduce an ultrahigh-resolution (50) 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.