Sparse Depth Sampling for Interventional 2-D/3-D Overlay: Theoretical Error Analysis and Enhanced Motion Estimation

Jian Wang, Christian Riess, Anja Borsdorf, Benno Heigl, Joachim Hornegger

Abstract: 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.