Deep computational model for the inference of ventricular activation properties
Deep computational model for the inference of ventricular activation properties
Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification. However, current workflows for both the anatomical and functional twinning phases, referring to the inference of model anatomy and parameter from clinical data, are not sufficiently efficient, robust, and accurate. In this work, we propose a deep learning-based patient-specific computational model, which can fuse both anatomical and electrophysiological information for the inference of ventricular activation properties, i.e., conduction velocities and root nodes. The activation properties can provide a quantitative assessment of cardiac electrophysiological function for the guidance of interventional procedures. We employ the Eikonal model to generate simulated electrocardiograms (ECGs) with ground truth properties to train the inference model, where patient-specific information has also been considered. For evaluation, we test the model on the simulated data and obtain generally promising results with fast computational time.
Deep computational models, Digital twin, ECG simulation, Ventricular activation properties
369-380
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Camps, Julia
3d855b40-6c59-4461-a930-2027bc5b5e48
Banerjee, Abhirup
38c4aeb6-9b4d-48cc-bd6b-82137dfbd295
Beetz, Marcel
8110d48b-e65e-408a-b126-6f63721dcc06
Rodriguez, Blanca
5957341d-390e-42f1-b725-8d625a1e539c
Grau, Vicente
c6992187-7840-45ce-93a0-d54d06031c7d
28 January 2023
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Camps, Julia
3d855b40-6c59-4461-a930-2027bc5b5e48
Banerjee, Abhirup
38c4aeb6-9b4d-48cc-bd6b-82137dfbd295
Beetz, Marcel
8110d48b-e65e-408a-b126-6f63721dcc06
Rodriguez, Blanca
5957341d-390e-42f1-b725-8d625a1e539c
Grau, Vicente
c6992187-7840-45ce-93a0-d54d06031c7d
Li, Lei, Camps, Julia, Banerjee, Abhirup, Beetz, Marcel, Rodriguez, Blanca and Grau, Vicente
(2023)
Deep computational model for the inference of ventricular activation properties.
Camara, Oscar, Puyol-Antón, Esther, Suinesiaputra, Avan, Young, Alistair, Qin, Chen, Sermesant, Maxime and Wang, Shuo
(eds.)
In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers (STACOM 2022).
vol. 13593 LNCS,
Springer Cham.
.
(doi:10.1007/978-3-031-23443-9_34).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification. However, current workflows for both the anatomical and functional twinning phases, referring to the inference of model anatomy and parameter from clinical data, are not sufficiently efficient, robust, and accurate. In this work, we propose a deep learning-based patient-specific computational model, which can fuse both anatomical and electrophysiological information for the inference of ventricular activation properties, i.e., conduction velocities and root nodes. The activation properties can provide a quantitative assessment of cardiac electrophysiological function for the guidance of interventional procedures. We employ the Eikonal model to generate simulated electrocardiograms (ECGs) with ground truth properties to train the inference model, where patient-specific information has also been considered. For evaluation, we test the model on the simulated data and obtain generally promising results with fast computational time.
This record has no associated files available for download.
More information
Published date: 28 January 2023
Additional Information:
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Venue - Dates:
13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, , Singapore, Singapore, 2022-09-18 - 2022-09-18
Keywords:
Deep computational models, Digital twin, ECG simulation, Ventricular activation properties
Identifiers
Local EPrints ID: 489059
URI: http://eprints.soton.ac.uk/id/eprint/489059
ISSN: 0302-9743
PURE UUID: 7cffd0f5-b72d-4884-8591-d1485f0dda72
Catalogue record
Date deposited: 11 Apr 2024 17:25
Last modified: 06 Jun 2024 02:20
Export record
Altmetrics
Contributors
Author:
Lei Li
Author:
Julia Camps
Author:
Abhirup Banerjee
Author:
Marcel Beetz
Author:
Blanca Rodriguez
Author:
Vicente Grau
Editor:
Oscar Camara
Editor:
Esther Puyol-Antón
Editor:
Avan Suinesiaputra
Editor:
Alistair Young
Editor:
Chen Qin
Editor:
Maxime Sermesant
Editor:
Shuo Wang
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