用扩散模型解决心脏电活动逆问题，无需解剖结构即可高精度重建。
Geometry-Free Conditional Diffusion Modeling for Solving the Inverse Electrocardiography Problem
- 基于条件扩散模型，从体表信号生成心表面电势的多解概率分布。
- 在真实数据集上优于CNN、LSTM和Transformer等确定性方法。
- 无需患者专属网格，适合临床快速部署，尤其适合无解剖信息场景。
本文提出一种数据驱动方法，用于求解心电图成像（ECGI）背后的逆问题。我们构建了一个条件扩散框架，学习从带有噪声的体表信号到心脏表面电势的随机映射。该方法利用扩散模型的生成能力，捕捉逆问题中解不唯一且欠定的本质，实现多个重构结果的概率采样，而非单一确定性估计。与传统方法不同，该框架完全几何无关且纯数据驱动，避免了患者特异性网格构建的需求。我们在真实ECGI数据集上评估该方法，并与强基准模型（包括卷积神经网络、长短期记忆网络和基于Transformer的模型）进行对比。实验结果表明，所提扩散方法在重构精度上表现更优，展示了扩散模型在非侵入式心脏电生理成像中的强大潜力。
原文摘要 · Abstract (English)
This paper proposes a data-driven model for solving the inverse problem of electrocardiography, the mathematical problem that forms the basis of electrocardiographic imaging (ECGI). We present a conditional diffusion framework that learns a probabilistic mapping from noisy body surface signals to heart surface electric potentials. The proposed approach leverages the generative nature of diffusion models to capture the non-unique and underdetermined nature of the ECGI inverse problem, enabling probabilistic sampling of multiple reconstructions rather than a single deterministic estimate. Unlike traditional methods, the proposed framework is geometry-free and purely data-driven, alleviating the need for patient-specific mesh construction. We evaluate the method on a real ECGI dataset and compare it against strong deterministic baselines, including a convolutional neural network, long short-term memory network, and transformer-based model. The results demonstrate that the proposed diffusion approach achieves improved reconstruction accuracy, highlighting the potential of diffusion models as a robust tool for noninvasive cardiac electrophysiology imaging.
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