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4D Shape Registration for Dynamic Electrophysiological Cardiac Mapping

  • Kevin Wilson
  • Gerard Guiraudon
  • Doug Jones
  • Terry M. Peters
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

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.

Keywords

Point Cloud Iterative Close Point Data Point Cloud Iterative Close Point Iterative Close Point Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kevin Wilson
    • 1
    • 5
  • Gerard Guiraudon
    • 4
    • 5
  • Doug Jones
    • 3
    • 4
  • Terry M. Peters
    • 1
    • 2
    • 5
  1. 1.Biomedical Engineering Program 
  2. 2.Department of Medical Biophysics 
  3. 3.Department of MedicineThe University of Western Ontario 
  4. 4.Canadian Surgical Technology and Advanced Robotics (CSTAR) 
  5. 5.Imaging Research LabsRobarts Research InstituteLondonCanada

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