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Fast uncertainty quantification of activation sequences in patient-specific cardiac electrophysiology meeting clinical time constraints


Quaglino, A; Pezzuto, S; Koutsourelakis, P S; Auricchio, A; Krause, R (2018). Fast uncertainty quantification of activation sequences in patient-specific cardiac electrophysiology meeting clinical time constraints. International Journal for Numerical Methods in Biomedical Engineering, 34(7):e2985.

Abstract

We present a fast, patient-specific methodology for uncertainty quantification in electrophysiology, aimed at meeting the time constraints of clinical practitioners. We focus on computing the statistics of the activation map, given the uncertainties associated with the conductivity tensor modeling the fiber orientation in the heart. We use a fast parallel solution method implemented on a graphics processing unit for the eikonal approximation, in order to compute the activation map and to sample the random fiber field with correlation on the basis of geodesic distances. While this enables to perform uncertainty quantification studies with a manageable computational effort, the required time frame still exceeds clinically suitable time expectations. In order to reduce it further by 2 orders of magnitude, we rely on Bayesian multifidelity methods. In particular, we propose a low-fidelity model that is patient-specific and free from the additional training cost associated with reduced models. This is achieved by a sound physics-based simplification of the full eikonal model. The low-fidelity output is then corrected by the standard multifidelity framework. In practice, the complete procedure only requires approximately 100 new runs of our eikonal graphics processing unit solver for producing the sought estimates and their associated credible intervals, enabling a full online analysis in less than 5 minutes.

Abstract

We present a fast, patient-specific methodology for uncertainty quantification in electrophysiology, aimed at meeting the time constraints of clinical practitioners. We focus on computing the statistics of the activation map, given the uncertainties associated with the conductivity tensor modeling the fiber orientation in the heart. We use a fast parallel solution method implemented on a graphics processing unit for the eikonal approximation, in order to compute the activation map and to sample the random fiber field with correlation on the basis of geodesic distances. While this enables to perform uncertainty quantification studies with a manageable computational effort, the required time frame still exceeds clinically suitable time expectations. In order to reduce it further by 2 orders of magnitude, we rely on Bayesian multifidelity methods. In particular, we propose a low-fidelity model that is patient-specific and free from the additional training cost associated with reduced models. This is achieved by a sound physics-based simplification of the full eikonal model. The low-fidelity output is then corrected by the standard multifidelity framework. In practice, the complete procedure only requires approximately 100 new runs of our eikonal graphics processing unit solver for producing the sought estimates and their associated credible intervals, enabling a full online analysis in less than 5 minutes.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Cardiocentro Ticino
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Physical Sciences > Software
Physical Sciences > Biomedical Engineering
Physical Sciences > Modeling and Simulation
Life Sciences > Molecular Biology
Physical Sciences > Computational Theory and Mathematics
Physical Sciences > Applied Mathematics
Language:English
Date:July 2018
Deposited On:19 Feb 2019 13:26
Last Modified:29 Jul 2020 08:14
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:2040-7939
OA Status:Closed
Publisher DOI:https://doi.org/10.1002/cnm.2985
PubMed ID:29577657

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