Statistical Model of Paroxysmal Atrial Fibrillation Catheter Ablation Targets for Pulmonary Vein Isolation

  • Ahmad Al-Agamy
  • Rashed Karim
  • Aruna Arujuna
  • James L. Harrison
  • Steven E. Williams
  • Kawal S. Rhode
  • Hans C. van Assen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8896)


Atrial fibrillation (AF) is the most common cardiac arrhythmia. Pulmonary vein isolation (PVI) by catheter ablation is a cornerstone treatment of paroxysmal AF. Low success rates are mainly due to reconnecting tissue. Local myocardial wall-thickness (WT) information is missing; lesion transmurality is impossible to estimate. WT information can be obtained from pencil beam high-resolution MRI, a time-consuming protocol. To reduce scan time, automatic selection of regions of interest is proposed. We developed a left atrial target probability model for paroxysmal AF ablation, based on intraprocedural ablation targeting data of fifteen patients, to support the selection of these regions. A common mesh serves as a reference for registration of the electroanatomical meshes and ablation targets using landmark registration and the Iterative Closest Points algorithm. This is followed by projection of the ablation targets onto the mean mesh model, closure of isolated ablation voids on the surface and Gaussian smoothing of the probability distribution.

The final probability distribution clearly shows PVI contours as suggested in the consensus statement by European associations. The right inferior pulmonary vein (RIPV) shows a lower ablation probability, which may be due to limited maneuverability of the ablation catheter and the proximity of the RIPV ostium and the transseptal puncture, where the catheter enters the left atrium.


Atrial fibrillation Left atrium Catheter ablation Pulmonary vein isolation Statistical modeling 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ahmad Al-Agamy
    • 1
  • Rashed Karim
    • 2
  • Aruna Arujuna
    • 2
  • James L. Harrison
    • 2
  • Steven E. Williams
    • 2
  • Kawal S. Rhode
    • 2
  • Hans C. van Assen
    • 1
  1. 1.Signal Processing Systems, Department of Electrical EngineeringEindhoven University of TechnologyEindhovenNetherlands
  2. 2.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK

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