Atrial Fibrillation
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25/Creating patient-specific atrial fibrillation models from imaging and electroanatomic mapping data

Published Online: October 9th 2012 European Journal of Arrhythmia & Electrophysiology. 2021;7(Suppl. 1):abstr25
Authors: GR Raffaele (Presenting Author) – Faculty of Life Sciences and Medicine, King’s College London, London; CHR Roney – School of Biomedical Engineering and Imaging Sciences, King’s College London; School of Engineering and Materials Science, Queen Mary University of London, London; IK Kotadia – School of Biomedical Engineering and Imaging Sciences, King’s College London, London; JAS-L Solis-Lemus – School of Biomedical Engineering and Imaging Sciences, King’s College London, London; IS Sim – School of Biomedical Engineering and Imaging Sciences, King’s College London, London; JW Whitaker – School of Biomedical Engineering and Imaging Sciences, King’s College London; The Department of Internal Medicine, Cardiovascular Division, Brigham and Women’s Hospital, Boston; OR Razeghi – School of Biomedical Engineering and Imaging Sciences, King’s College London, London; MO O’Neill – School of Biomedical Engineering and Imaging Sciences, King’s College London, London; SEW Williams – School of Biomedical Engineering and Imaging Sciences, King’s College London, London; Centre for Cardiovascular Science, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh; SMN Narayan – Department of Medicine and Cardiovascular Institute, Stanford University, Palo Alto; SAN Niederer – School of Biomedical Engineering and Imaging Sciences, King’s College London, London
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Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting over 1.4 million people in the UK alone, and is associated with an increased risk of cardiovascular diseases, stroke and death. AF is often treated by catheter ablation therapy, which aims to isolate regions of the atrial tissue that are critical for driving the arrhythmia. However, it is challenging to identify these critical regions and target catheter ablation therapy, which is one of the reasons that AF treatment responses are suboptimal. Patient-specific simulations can be used to investigate the mechanisms underlying arrhythmias and test patient-specific therapies across cohorts of patients. This study aimed to develop and test CemrgApp for creating patient-specific left atrial models from MRI images or CT images and electroanatomic mapping data.

Methods and results: We developed and used tools within CemrgApp software, which is an interactive medical imaging application with image processing toolkits (cemrgapp.com), to build atrial models from MRI or CT imaging data and electroanatomic mapping data. We used CemrgApp to post-process each surface mesh to identify the pulmonary vein structures, appendage, and mitral value to create a labelled mesh (Figure 1). We also clipped the structures, remeshed the shell to simulation resolution, and selected landmark points. We calculated universal atrial coordinates to register atrial fibre fields from an atlas. AF simulations were then run on the models using Cardiac Arrhythmia Research Package (CARP) software, which were post-processed to generate phase singularity density maps indicating potential driver site locations over 15-second simulations. We built 44 patient-specific meshes and simulated AF across this cohort of models.

Conclusion: We have developed an open-source pipeline in CemrgApp for constructing personalised left atrial models. Our future work will use this pipeline to construct large cohorts of models for virtual in silico trials to test different ablation and antiarrhythmic drug therapies for atrial fibrillation. 

Figure 1

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