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Using the Universal Atrial Coordinate System for MRI and Electroanatomic Data Registration in Patient-Specific Left Atrial Model Construction and Simulation

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Marianne Beach, Iain Sim, Arihant Mehta, Irum Kotadia, Daniel O’Hare, John Whitaker, Jose Alonso Solis-Lemus, Orod Razeghi, Amedeo Chiribiri, Mark O’Neill, Steven Williams, Steven A. Niederer, Caroline H. Roney

Original languageEnglish
Title of host publicationFunctional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings
EditorsDaniel B. Ennis, Luigi E. Perotti, Vicky Y. Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030787097
Event11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021 - Virtual, Online
Duration: 21 Jun 202125 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12738 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021
CityVirtual, Online

Bibliographical note

Funding Information: Acknowledgement. CR is funded by an MRC Skills Development Fellowship (MR/S015086/1). SN acknowledges support from the EPSRC (EP/M012492/1, NS/A000049/1, and EP/P01268X/1), the British Heart Foundation (PG/15/91/31812, PG/13/37/30280), and Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre. SW acknowledges a British Heart Foundation Fellowship (FS 20/26/34952). This work was supported by the Wellcome/EPSRC Centre for Medical Engineering (WT 203148/Z/16/Z). Publisher Copyright: © 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


Current biophysical atrial models for investigating atrial fibrillation (AF) mechanisms and treatment approaches use imaging data to define patient-specific anatomy. Electrophysiology of the models can be calibrated using invasive electrical data collected using electroanatomic mapping (EAM) systems. However, these EAM data are typically only available after the catheter ablation procedure has begun, which makes it challenging to use personalised biophysical simulations for informing procedures. In this study, we first aimed to derive a mapping between LGE-MRI intensity and EAM conduction velocity (CV) for calibrating patient-specific left atrial electrophysiology models. Second, we investigated the functional effects of this calibration on simulated arrhythmia properties. To achieve this, we used the Universal Atrial Coordinate (UAC) system to register LGE-MRI and EAM meshes for ten patients. We then post-processed these data to investigate the relationship between LGE-MRI intensities and EAM CV. Mean atrial CV decreased from 0.81 ± 0.31 m/s to 0.58 ± 0.18 m/s as LGE-MRI image intensity ratio (IIR) increased from IIR < 0.9 to 1.6 ≤ IIR. The relationship between IIR and CV was used to calibrate conductivity for a cohort of 50 patient-specific models constructed from LGE-MRI data. This calibration increased the mean number of phase singularities during simulated arrhythmia from 2.67 ± 0.94 to 5.15 ± 2.60.

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