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Virtual Catheter Ablation of Target Areas Identified from Image-Based Models of Atrial Fibrillation

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Functional Imaging and Modeling of the Heart (FIMH 2019)

Abstract

Catheter Ablation (CA) is an effective strategy for rhythm control in atrial fibrillation (AF) patients. However, success rate remains suboptimal in chronic AF patients, where targets for optimal ablation are unknown. Recent clinical evidence suggests an association of atrial fibrosis with locations of re-entrant drivers (RDs) sustaining AF. However, the knowledge of optimal ablation locations based on patient-specific fibrosis distribution is lacking. The aim of this study is to provide a proof-of-concept method to (1) predict patient-specific ablation targets from 3D models of fibrotic atria and (2) perform virtual ablation.

Left atrial (LA) geometry and fibrosis distribution of a persistent AF patient was obtained from MR imaging data. AF simulations were performed by initiating RDs at 12 different locations in the LA model. The tip of the meandering RDs was tracked in all simulations to identify atrial wall regions with the highest probability of harbouring RDs – target areas (TAs). Finally, virtual ablation was performed based on the knowledge of TAs to identify strategies that eliminate RDs.

Our simulations showed that the TAs are typically located at specific regions within the fibrotic patches where RDs stabilize. Ablation strategies that connect these TAs to the nearest pulmonary vein (PV) or the mitral valve can both terminate the existing RD and reduce the inducibility of new RDs, thus preventing AF.

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Acknowledgements

This work was supported by the British Heart Foundation [PG/15/8/31138], the EPSRC [EP/L015226/1, EP/R511559/1] and the Wellcome Trust/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z].

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Correspondence to Aditi Roy .

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Roy, A. et al. (2019). Virtual Catheter Ablation of Target Areas Identified from Image-Based Models of Atrial Fibrillation. In: Coudière, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds) Functional Imaging and Modeling of the Heart. FIMH 2019. Lecture Notes in Computer Science(), vol 11504. Springer, Cham. https://doi.org/10.1007/978-3-030-21949-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-21949-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21948-2

  • Online ISBN: 978-3-030-21949-9

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