期刊
MEDICAL IMAGE ANALYSIS
卷 73, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.media.2021.102143
关键词
Electrocardiographic imaging; Bayesian inference; Digital twin; Electrocardiogram
类别
资金
- Scatcherd European Scholarship
- Engineering and Physical Sciences Research Council
- Wellcome Trust Fellowship in Basic Biomedical Sciences [214290/Z/18/Z]
- CompBioMed 2 Centre of Excellence in Computational Biomedicine (European Commission Horizon 2020 research and innovation programme) [823712]
- Australian Research Council Centre of Excellence for Mathe-matical and Statistical Frontiers [CE140100049]
- Australian Research Council Discovery Project [DP200102101]
- Amazon Web Services Machine Learning Research Award [364348137979]
- PRACE ICEI project [icp005]
- Amazon Web Services
- Wellcome Trust [214290/Z/18/Z]
- Wellcome Trust [214290/Z/18/Z] Funding Source: Wellcome Trust
Precision cardiology requires novel techniques for non-invasive characterization of individual cardiac function and the development of the cardiac 'digital twin'. This study introduces new computational methods for estimating key ventricular activation properties for individual subjects.
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 cm 3 to 171 cm 3 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. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
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