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arXiv · 2406.18063

Data-driven imaging geometric recovery of ultrahigh resolution robotic micro-CT for in-vivo and other applications

Mengzhou Li·Guibin Zan·Wenbin Yun·Josef Uher·John Wen·Ge Wang
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Abstract

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.

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Mengzhou Li, Guibin Zan, Wenbin Yun, Josef Uher, John Wen, Ge Wang. 2024-06-26. Data-driven imaging geometric recovery of ultrahigh resolution robotic micro-CT for in-vivo and other applications. https://arxiv.org/abs/2406.18063

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