Abstract
Cardiac digital twins (CDTs) provide personalized in-silico cardiac representations and hold great potential for precision medicine in cardiology. However, whole-heart CDT models that simulate the full organ-scale electromechanics of all four heart chambers remain limited. In this work, we propose a weakly supervised learning model to reconstruct 4D (3D+t) heart mesh directly from multi-view 2D cardiac cine MRIs. This is achieved by learning a self-supervised mapping between cine MRIs and 4D cardiac meshes, enabling the generation of personalized heart models that closely correspond to input cine MRIs. The resulting 4D heart meshes can facilitate the automatic extraction of key cardiac variables, including ejection fraction and dynamic chamber volume changes with high temporal resolution. It demonstrates the feasibility of inferring personalized 4D heart models from cardiac MRIs, paving the way for an efficient CDT platform for precision medicine. The code will be publicly released once the manuscript is accepted.
Abstract (translated)
心脏数字孪生（CDTs）提供个性化的体内心脏表示，并在心脏病学的精准医学中具有巨大潜力。然而，目前全心CDT模型仍受限于难以模拟所有四个心室的整体器官规模电机械特性。在这项工作中，我们提出了一种弱监督学习模型，可以从多视角2D心脏动态MRI（cine MRIs）直接重建4D（3D+t）心脏网格。通过学习动态MRI与4D心脏网格之间的自监督映射关系，该方法能够生成与输入动态MRI紧密对应的个性化心脏模型。由此产生的4D心脏网格可以自动提取关键心脏变量，包括射血分数和心室容积的动态变化，并具有高时间分辨率。这表明从心脏磁共振图像中推断个性化的4D心脏模型是可行的，为精准医学中的高效CDT平台铺平了道路。一旦论文被接受，代码将公开发布。
URL
https://arxiv.org/abs/2507.15203
