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: $1.24 \pm 0.293$ 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 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.

翻译：心脏数字孪生（CDTs）为心脏机制相关的多尺度特性推断提供了个性化的在体内心脏表征。构建CDTs需要关于躯干上电极位置的精确信息，尤其对于个性化心电图（ECG）校准至关重要。然而，现有研究通常依赖于额外采集躯干影像以及手动/半自动方法进行ECG电极定位。本研究提出了一种新颖高效的拓扑信息模型，能够从二维临床标准心脏MRI中全自动提取个性化的标准ECG电极位置。具体而言，我们从心脏MRI中获取稀疏的躯干轮廓，然后从这些轮廓中定位12导联ECG的标准电极。心脏MRI主要针对心脏而非躯干成像，导致影像中的躯干几何结构不完整。为解决拓扑信息缺失问题，我们将电极作为关键点子集进行整合，使其能够与三维躯干拓扑结构显式对齐。实验结果表明，所提模型在精度（欧氏距离：$1.24 \pm 0.293$ cm 对比 $1.48 \pm 0.362$ cm）和效率（$2$~秒 对比 $30$-$35$~分钟）方面均优于耗时的传统模型投影方法。我们进一步验证了使用检测到的电极进行在体内心电图模拟的有效性，凸显了其在构建精准高效CDT模型方面的潜力。代码发布于 https://github.com/lileitech/12lead_ECG_electrode_localizer。