Dreaming of Electrical Waves: 基于扩散模型的心脏兴奋波生成建模
Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models
AI总结：
本研究探索用去噪扩散概率模型生成心脏电波模式，可实现螺旋波的生成、演化、修复及三维重建等任务，效果接近生物物理模型，但存在数据不足时产生伪影的问题。
摘要：
在心房颤动或心室颤动等危及生命的心律失常期间，心脏中的电波会形成旋转的螺旋波或卷轴波。这类波的动力学通常使用耦合偏微分方程建模，这些方程描述了可激发介质中的反应-扩散动力学。近年来，数据驱动的生成建模已成为在物理和生物系统中生成时空模式的替代方法。本研究探索了将去噪扩散概率模型用于心脏组织中电波模式的生成建模。我们用模拟的电波模式训练扩散模型，使其能够在无条件和条件生成任务中生成此类波模式。例如，我们探索了基于扩散的螺旋波动力学的三类应用：i）参数特定生成、ii）演化和iii）修复，其中包括从浅表二维测量重建三维卷轴波动力学。此外，我们生成了任意形状的双心室几何结构，并使用扩散模型在这些几何结构内同时引发卷轴波模式。我们对扩散生成的解进行了表征，并将其与相应生物物理模型得到的解进行了比较，发现扩散模型能够很好地学习复制螺旋波和卷轴波的动力学，可用于心脏组织中兴奋波的数据驱动建模。例如，一组扩散生成的螺旋波动力学表现出的自终止统计特征，与用生物物理模型模拟的对应组的自终止统计特征相似。但我们也发现，如果训练数据不足（例如在自终止过程中），扩散模型会产生伪影，并且在约束不足时会“幻觉”出波模式。
英文摘要：
Electrical waves in the heart form rotating spiral or scroll waves during life-threatening arrhythmias such as atrial or ventricular fibrillation. The wave dynamics are typically modeled using coupled partial differential equations, which describe reaction-diffusion dynamics in excitable media. More recently, data-driven generative modeling has emerged as an alternative to generate spatio-temporal patterns in physical and biological systems. Here, we explore denoising diffusion probabilistic models for the generative modeling of electrical wave patterns in cardiac tissue. We trained diffusion models with simulated electrical wave patterns to be able to generate such wave patterns in unconditional and conditional generation tasks. For instance, we explored the diffusion-based i) parameter-specific generation, ii) evolution and iii) inpainting of spiral wave dynamics, including reconstructing three-dimensional scroll wave dynamics from superficial two-dimensional measurements. Further, we generated arbitrarily shaped bi-ventricular geometries and simultaneously initiated scroll wave patterns inside these geometries using diffusion. We characterized and compared the diffusion-generated solutions to solutions obtained with corresponding biophysical models and found that diffusion models learn to replicate spiral and scroll waves dynamics so well that they could be used for data-driven modeling of excitation waves in cardiac tissue. For instance, an ensemble of diffusion-generated spiral wave dynamics exhibits similar self-termination statistics as the corresponding ensemble simulated with a biophysical model. However, we also found that diffusion models {produce artifacts if training data is lacking, e.g. during self-termination,} and `hallucinate' wave patterns when insufficiently constrained.
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