2013 | OriginalPaper | Buchkapitel
Sparse Depth Sampling for Interventional 2-D/3-D Overlay: Theoretical Error Analysis and Enhanced Motion Estimation
verfasst von : Jian Wang, Christian Riess, Anja Borsdorf, Benno Heigl, Joachim Hornegger
Erschienen in: Computer Analysis of Images and Patterns
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Patient motion compensation is challenging for dynamic 2-D/3-D overlay in interventional procedures. A first motion compensation approach based on depth-layers has been recently proposed, where 3-D motion can be estimated by tracking feature points on 2-D X-ray images. However, the sparse depth estimation introduces a systematic error. In this paper, we present a theoretical analysis on the systematic error and propose an enhanced motion estimation strategy accordingly. The simulation experiments show that the proposed approach yields a reduced 3-D correction error that is consistently below 2 mm, in comparison to a mean of 6 mm with high variance using the previous approach.