使用深度计算模型进行逆向推断以实现心肌梗死心脏数字孪生
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference
AI总结：
本文提出一种结合双分支变分自编码器的深度计算模型，通过整合心脏MRI和ECG等多模态数据，从模拟QRS波群逆向推断心肌梗死位置与分布，为个性化心脏数字孪生提供可行方案。
摘要：
心脏数字孪生（CDTs）有望以无创方式提供心脏功能的个体化评估，使其成为心肌梗死（MI）个性化诊断和治疗规划的一种有前景的方法。推断准确的心肌组织特性对于创建可靠的MI CDT至关重要。在这项工作中，我们研究了在CDT平台内从心电图（ECG）推断心肌组织特性的可行性。该平台整合了多模态数据（如心脏MRI和ECG），以提高推断组织特性的准确性和可靠性。我们基于计算机模拟进行了敏感性分析，系统探讨了梗死位置、大小、透壁程度和电活动改变对模拟ECG QRS波群的影响，以确立该方法的局限性。随后，我们提出了一种新型深度计算模型，包含双分支变分自编码器和推断模型，用于从模拟的QRS波群推断梗死位置和分布。所提出的模型在左心室瘢痕和边缘区推断中分别实现了0.457 ± 0.317和0.302 ± 0.273的平均Dice分数。敏感性分析增强了我们对梗死特征与电生理特征之间复杂关系的理解。计算机模拟实验结果表明，该模型能够有效捕捉这种关系以进行逆向推断，在未来具有广阔的临床应用前景。代码将在稿件被录用发表后公开发布。
英文摘要：
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 my-ocardial 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 ac-tivity 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 \pm 0.317 and 0.302 \pm 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 will be released publicly once the manuscript is accepted for publication.
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