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Personalized topology-informed localization of standard 12-lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins

Abstract:
Cardiac digital twins (CDTs) offer personalized in-silico cardiac representations for the inference of multi-scale properties tied to cardiac mechanisms. The creation of CDTs requires precise information about the electrode position on the torso, especially for the personalized electrocardiogram (ECG) calibration. However, current studies commonly rely on additional acquisition of torso imaging and manual/semi-automatic methods for ECG electrode localization. In this study, we propose a novel and efficient topology-informed model to fully automatically extract personalized ECG standard electrode locations from 2D clinically standard cardiac MRIs. Specifically, we obtain the sparse torso contours from the cardiac MRIs and then localize the standard electrodes of 12-lead ECG from the contours. Cardiac MRIs aim at imaging of the heart instead of the torso, leading to incomplete torso geometry within the imaging. To tackle the missing topology, we incorporate the electrodes as a subset of the keypoints, which can be explicitly aligned with the 3D torso topology. The experimental results demonstrate that the proposed model outperforms the time-consuming conventional model projection-based method in terms of accuracy (Euclidean distance: 1.24±0.293 cm vs. 1.48±0.362 cm) and efficiency (2 s vs. 30-35 min). We further demonstrate the effectiveness of using the detected electrodes for in-silico ECG simulation, highlighting their potential for creating accurate and efficient CDT models. The code is available at https://github.com/lileitech/12lead_ECG_electrode_localizer.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.media.2025.103472

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author


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Funder identifier:
https://ror.org/029chgv08
Grant:
102161/B/13/Z
214290/Z/18/Z
More from this funder
Funder identifier:
https://ror.org/03wnrjx87


Publisher:
Elsevier
Journal:
Medical Image Analysis More from this journal
Volume:
101
Article number:
103472
Publication date:
2025-01-21
Acceptance date:
2025-01-13
DOI:
EISSN:
1361-8423
ISSN:
1361-8415
Pmid:
39854816


Language:
English
Keywords:
Pubs id:
2080558
Local pid:
pubs:2080558
Deposit date:
2025-03-20
ARK identifier:

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