Personalized Topology-Informed 12-Lead ECG Electrode Localization from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins
<|reference_start|>Personalized Topology-Informed 12-Lead ECG Electrode Localization from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins: Cardiac digital twins (CDTs) offer personalized \textit{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 electrode locations from 2D clinically standard cardiac MRIs. Specifically, we obtain the sparse torso contours from the cardiac MRIs and then localize the electrodes 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 method in terms of accuracy (Euclidean distance: $1.24 \pm 0.293$ cm vs. $1.48 \pm 0.362$ cm) and efficiency ($2$~s vs. $30$-$35$~min). We further demonstrate the effectiveness of using the detected electrodes for \textit{in-silico} ECG simulation, highlighting their potential for creating accurate and efficient CDT models. The code will be released publicly after the manuscript is accepted for publication.<|reference_end|>
@article{li2024personalized,
    title={Personalized Topology-Informed 12-Lead ECG Electrode Localization from
  Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins},
    author={Lei Li, Hannah Smith, Yilin Lyu, Julia Camps, Blanca Rodriguez,
  Abhirup Banerjee, Vicente Grau},
    journal={arXiv preprint arXiv:2408.13945},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13945},
    primaryClass={eess.IV cs.CV physics.med-ph}
}