Automatic reconstruction of the left atrium activation from sparse intracardiac contact recordings by inverse estimate of fibre structure and anisotropic conduction in a patient-specific model.

Atrial fibres Conduction velocity Conductivity tensor Electro-anatomic mapping Patient-specific modelling

Journal

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
ISSN: 1532-2092
Titre abrégé: Europace
Pays: England
ID NLM: 100883649

Informations de publication

Date de publication:
04 03 2021
Historique:
received: 20 11 2020
accepted: 07 12 2020
entrez: 22 3 2021
pubmed: 23 3 2021
medline: 10 8 2021
Statut: ppublish

Résumé

Electric conduction in the atria is direction-dependent, being faster in fibre direction, and possibly heterogeneous due to structural remodelling. Intracardiac recordings of atrial activation may convey such information, but only with high-quality data. The aim of this study was to apply a patient-specific approach to enable such assessment even when data are scarce, noisy, and incomplete. Contact intracardiac recordings in the left atrium from nine patients who underwent ablation therapy were collected before pulmonary veins isolation and retrospectively included in the study. The Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps (PIEMAP), previously developed, has been used to reconstruct the conductivity tensor from sparse recordings of the activation. Regional fibre direction and conduction velocity were estimated from the fitted conductivity tensor and extensively cross-validated by clustered and sparse data removal. Electrical conductivity was successfully reconstructed in all patients. Cross-validation with respect to the measurements was excellent in seven patients (Pearson correlation r > 0.93) and modest in two patients (r = 0.62 and r = 0.74). Bland-Altman analysis showed a neglectable bias with respect to the measurements and the limit-of-agreement at -22.2 and 23.0 ms. Conduction velocity in the fibre direction was 82 ± 25 cm/s, whereas cross-fibre velocity was 46 ± 7 cm/s. Anisotropic ratio was 1.91±0.16. No significant inter-patient variability was observed. Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps correctly predicted activation times in late regions in all patients (r = 0.88) and was robust to a sparser dataset (r = 0.95). Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps offers a novel approach to extrapolate the activation in unmapped regions and to assess conduction properties of the atria. It could be seamlessly integrated into existing electro-anatomic mapping systems. Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps also enables personalization of cardiac electrophysiology models.

Identifiants

pubmed: 33751078
pii: 6158558
doi: 10.1093/europace/euaa392
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

i63-i70

Informations de copyright

Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.

Auteurs

Jolijn M Lubrecht (JM)

Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.
Department of Physiology, CARIM, Maastricht University, Maastricht, The Netherlands.

Thomas Grandits (T)

Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.
BioTechMed Graz, Graz, Austria.

Ali Gharaviri (A)

Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.

Ulrich Schotten (U)

Department of Physiology, Maastricht University, Maastricht, The Netherlands.

Thomas Pock (T)

Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.
BioTechMed Graz, Graz, Austria.

Gernot Plank (G)

BioTechMed Graz, Graz, Austria.
Institute of Biophysics, Medical University of Graz, Graz, Austria.

Rolf Krause (R)

Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.

Angelo Auricchio (A)

Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.
Division of Cardiology, Fondazione Cardiocentro Ticino, Lugano, Switzerland.

Giulio Conte (G)

Division of Cardiology, Fondazione Cardiocentro Ticino, Lugano, Switzerland.

Simone Pezzuto (S)

Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.

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