A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs

• Published in: Medical image analysis Vol. 71; p. 102080
• Main Authors: Gillette, Karli, Gsell, Matthias A.F., Prassl, Anton J., Karabelas, Elias, Reiter, Ursula, Reiter, Gert, Grandits, Thomas, Payer, Christian, Štern, Darko, Urschler, Martin, Bayer, Jason D., Augustin, Christoph M., Neic, Aurel, Pock, Thomas, Vigmond, Edward J., Plank, Gernot
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2021; Elsevier BV
• Subjects: Accuracy; Cardiac digital twins; Computer Simulation; Decision making; Digital twins; EKG; Electrocardiography; Electrophysiologic Techniques, Cardiac; Electrophysiology; Forward ECG modeling; Heart; Heart Ventricles; Humans; Industrial applications; Inference; Magnetic resonance imaging; Mathematical models; Multi-label image segmentation; Parameter identification; Parameters; Saltelli sampling; Ventricle; Ventricular activation and repolarization sequence; Workflow; Ventricular activation and repolarization sequence; Saltelli sampling; Cardiac digital twins; Parameter identification; Forward ECG modeling; Multi-label image segmentation
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2021.102080

•Model reference frame allowing unattended manipulation of spatiallyvarying parameters.•Comprehensive physiological feature vector of ventricular EP during sinusrhythm.•Fast-forward ECG model producing gold-standard ECGs with real-time performance.•Automated workflow for generation of cardiac digital twins demonstrating feasibility. Cardiac digital twins (Cardiac Digital Twin (CDT)s) of human electrophysiology (Electrophysiology (EP)) are digital replicas of patient hearts derived from clinical data that match like-for-like all available clinical observations. Due to their inherent predictive potential, CDTs show high promise as a complementary modality aiding in clinical decision making and also in the cost-effective, safe and ethical testing of novel EP device therapies. However, current workflows for both the anatomical and functional twinning phases within CDT generation, referring to the inference of model anatomy and parameters from clinical data, are not sufficiently efficient, robust and accurate for advanced clinical and industrial applications. Our study addresses three primary limitations impeding the routine generation of high-fidelity CDTs by introducing; a comprehensive parameter vector encapsulating all factors relating to the ventricular EP; an abstract reference frame within the model allowing the unattended manipulation of model parameter fields; a novel fast-forward electrocardiogram (Electrocardiogram (ECG)) model for efficient and bio-physically-detailed simulation required for parameter inference. A novel workflow for the generation of CDTs is then introduced as an initial proof of concept. Anatomical twinning was performed within a reasonable time compatible with clinical workflows (<4h) for 12 subjects from clinically-attained magnetic resonance images. After assessment of the underlying fast forward ECG model against a gold standard bidomain ECG model, functional twinning of optimal parameters according to a clinically-attained 12 lead ECG was then performed using a forward Saltelli sampling approach for a single subject. The achieved results in terms of efficiency and fidelity demonstrate that our workflow is well-suited and viable for generating biophysically-detailed CDTs at scale.