The University of Southampton
University of Southampton Institutional Repository

Towards enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference

Towards enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference
Towards enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference

Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of 0.457 ± 0.317 and 0.302 ± 0.273 for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https: //github.com/lileitech/MI_inverse_inference.

Cardiac digital twins, cardiac MRI, Computational modeling, Electrocardiography, electrophysiology, Heart, Image reconstruction, inverse problem, Inverse problems, Magnetic resonance imaging, multi-modal integration, Myocardium
0278-0062
2466-2478
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Camps, Julia
3d855b40-6c59-4461-a930-2027bc5b5e48
Wang, Zhinuo
9d4da7bd-f364-41f1-bff4-191f31b06042
Beetz, Marcel
8110d48b-e65e-408a-b126-6f63721dcc06
Banerjee, Abhirup
38c4aeb6-9b4d-48cc-bd6b-82137dfbd295
Rodriguez, Blanca
5957341d-390e-42f1-b725-8d625a1e539c
Grau, Vicente
c6992187-7840-45ce-93a0-d54d06031c7d
et al.
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Camps, Julia
3d855b40-6c59-4461-a930-2027bc5b5e48
Wang, Zhinuo
9d4da7bd-f364-41f1-bff4-191f31b06042
Beetz, Marcel
8110d48b-e65e-408a-b126-6f63721dcc06
Banerjee, Abhirup
38c4aeb6-9b4d-48cc-bd6b-82137dfbd295
Rodriguez, Blanca
5957341d-390e-42f1-b725-8d625a1e539c
Grau, Vicente
c6992187-7840-45ce-93a0-d54d06031c7d

Li, Lei, Camps, Julia and Wang, Zhinuo , et al. (2024) Towards enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference. IEEE Transactions on Medical Imaging, 43 (7), 2466-2478. (doi:10.1109/TMI.2024.3367409).

Record type: Article

Abstract

Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of 0.457 ± 0.317 and 0.302 ± 0.273 for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https: //github.com/lileitech/MI_inverse_inference.

This record has no associated files available for download.

More information

Accepted/In Press date: 2024
e-pub ahead of print date: 19 February 2024
Published date: July 2024
Additional Information: Publisher Copyright: © 1982-2012 IEEE.
Keywords: Cardiac digital twins, cardiac MRI, Computational modeling, Electrocardiography, electrophysiology, Heart, Image reconstruction, inverse problem, Inverse problems, Magnetic resonance imaging, multi-modal integration, Myocardium

Identifiers

Local EPrints ID: 488799
URI: http://eprints.soton.ac.uk/id/eprint/488799
ISSN: 0278-0062
PURE UUID: ece90cbe-c02f-4f36-a13b-2ccb12064023
ORCID for Lei Li: ORCID iD orcid.org/0000-0003-1281-6472

Catalogue record

Date deposited: 05 Apr 2024 16:44
Last modified: 20 Jul 2024 02:13

Export record

Altmetrics

Contributors

Author: Lei Li ORCID iD
Author: Julia Camps
Author: Zhinuo Wang
Author: Marcel Beetz
Author: Abhirup Banerjee
Author: Blanca Rodriguez
Author: Vicente Grau
Corporate Author: et al.

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×