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.
Highlights
Cardiovascular diseases account for 31% of deaths globally, according to the World Health Organisation (2016)
This study presents an efficient inference method combined with fast ECG simulations using cardiac magnetic resonance (CMR)-based torso-biventricular models to determine the accuracy in the estimation of ventricular activation properties from the 12-lead ECG or activation time maps
We propose the application of an sequential Monte Carlo approximate Bayesian computation (SMC-ABC) (Appendix A.5) (Sisson, Fan, and Tanaka 2007; Drovandi and Pettitt 2011) algorithm that we designed to explore our mixed-type parameter space efficiently
Summary
Cardiovascular diseases account for 31% of deaths globally, according to the World Health Organisation (2016). Cardiac disease increases the risk of sudden and premature death through alterations in cardiac electrophysiology and tissue structure, which are known to promote lethal arrhythmias and mechanical dysfunction. The electrocardiogram (ECG) is the most widely used modality for diagnosis. The information that can be extracted from the ECG is, confounded by anatomical and functional variability in the human population. Non-invasive imaging, through ultrasound, computerised tomography or cardiac magnetic resonance (CMR), is used clinically to provide further information on cardiac anatomy, structure and mechanical function.
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