title: 4D shape registration for dynamic electrophysiological cardiac mapping.
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 degrees vs. 3D errors: Translation 1.2mm, Rotation 1.3 degrees) while also increasing capture range and success
authors.affiliation: Biomedical Engineering Program, The University of Western Ontario, Canada. kwilson@imaging.robarts.ca
authors.forename: Kevin
authors.initials: K
authors.lastname: Wilson
authors.forename: Gerard
authors.initials: G
authors.lastname: Guiraudon
authors.forename: Doug
authors.initials: D
authors.lastname: Jones
authors.forename: Terry M
authors.initials: TM
authors.lastname: Peters
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D018780:Body Surface Potential Mapping / Q000379:methods*; D003198:Computer Simulation; D006329:Heart Conduction System / Q000033:anatomy & histology* / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique