4D shape registration for dynamic electrophysiological cardiac mapping

Authors:
Kevin Wilson;Gerard Guiraudon;Doug Jones;Terry M. Peters
Affiliations:
Biomedical Engineering Program;Canadian Surgical Technology and Advanced Robotics (CSTAR);Department of Medicine, The University of Western Ontario;Biomedical Engineering Program
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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Abstract

Registration of 3D segmented cardiac images with tracked electrophysiological data has been previously investigated for use in cardiac mapping and navigation systems. However, dynamic cardiac 4D (3D + time) registration methods do not presently exist. This paper introduces two new 4D registration methods based on the popular iterative closest point (ICP) algorithm that may be applied to dynamic 3D shapes. The first method averages the transformations of the 3D ICP on each phase of the dynamic data, while the second finds the closest point pairs for the data in each phase and performs a least squares fit between all the pairs combined. Experimental results show these methods yield more accurate transformations compared to using a traditional 3D approach (4D errors: Translation 0.4mm, Rotation 0.45° vs. 3D errors: Translation 1.2mm, Rotation 1.3°) while also increasing capture range and success rate.