TY - JOUR
T1 - A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs
AU - Gillette, Karli
AU - Gsell, Matthias A.F.
AU - Prassl, Anton J.
AU - Karabelas, Elias
AU - Reiter, Ursula
AU - Reiter, Gert
AU - Grandits, Thomas
AU - Payer, Christian
AU - Štern, Darko
AU - Urschler, Martin
AU - Bayer, Jason D.
AU - Augustin, Christoph M.
AU - Neic, Aurel
AU - Pock, Thomas
AU - Vigmond, Edward J.
AU - Plank, Gernot
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
KW - Cardiac digital twins
KW - Forward ECG modeling
KW - Multi-label image segmentation
KW - Parameter identification
KW - Saltelli sampling
KW - Ventricular activation and repolarization sequence
UR - http://www.scopus.com/inward/record.url?scp=85105516977&partnerID=8YFLogxK
U2 - 10.1016/j.media.2021.102080
DO - 10.1016/j.media.2021.102080
M3 - Article
C2 - 33975097
AN - SCOPUS:85105516977
SN - 1361-8415
VL - 71
JO - Medical Image Analysis
JF - Medical Image Analysis
M1 - 102080
ER -