Elsevier

Medical Image Analysis

Volume 73, October 2021, 102143
Medical Image Analysis

Inference of ventricular activation properties from non-invasive electrocardiography

https://doi.org/10.1016/j.media.2021.102143Get rights and content
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Highlights

  • Estimation of the ventricular speeds and earliest activation sites from ECG and CMR.

  • Evaluation with twenty virtual subjects shows the effect of anatomical variability.

  • Bayesian-inspired simultaneous estimation of continuous and discrete parameters.

  • Efficient dynamic time warping-based comparison of electrocardiograms (ECG).

  • Changing fibre and sheet-normal speed does not affect healthy activation sequence.

Abstract

The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients’ cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The integration of multi-modal datasets through advanced computational methods could enable the development of the cardiac ‘digital twin’, a comprehensive virtual tool that mechanistically reveals a patient's heart condition from clinical data and simulates treatment outcomes. The adoption of cardiac digital twins requires the non-invasive efficient personalisation of the electrophysiological properties in cardiac models. This study develops new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography, cardiac magnetic resonance (CMR) imaging and modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical CMR imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac ventricular myocardial-mass volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties in sinus rhythm from non-invasive epicardial activation time maps and ECG recordings, achieving higher accuracy for the endocardial speed and sheet (transmural) speed than for the fibre or sheet-normal directed speeds.

Keywords

Electrocardiographic imaging
Bayesian inference
Digital twin
Electrocardiogram

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