从心脏MRI重建4D个性化心模型，助力精准医疗
Personalized 4D Whole Heart Geometry Reconstruction from Cine MRI for Cardiac Digital Twins
- 通过弱监督学习直接从多视角2D MRI重建4D心结构
- 可高精度提取射血分数和动态腔室容积变化
- 为构建高效心脏数字孪胎平台提供技术基础
心脏数字孪胎（CDT）可提供个性化的计算心脏模型，在心脏病精准医疗中具有巨大潜力。然而，能模拟四个心腔全器官尺度电机械特性的完整心脏CDT模型仍十分有限。本文提出一种弱监督学习模型，直接从多视角2D心脏电影MRI重建4D（3D+t）心脏网格。该方法通过学习电影MRI与4D心脏网格间的自监督映射，生成与输入MRI高度匹配的个性化心模型。重建的4D心模型支持高时间分辨率下自动提取关键心脏变量，包括射血分数及动态腔室容积变化。结果验证了从心脏MRI推断个性化4D心模型的可行性，为构建高效心脏数字孪胎平台奠定了基础。代码将在论文被接受后公开。
原文摘要 · Abstract (English)
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.
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