用文本提示引导生成图像，提升电导断层成像重建精度
SDEIT: Semantic-Driven Electrical Impedance Tomography
- 通过自然语言提示作为语义先验，结合扩散模型生成图像指导重建
- 在模拟与实验数据上均超越现有方法，结构一致性与细节恢复更优
- 无需配对训练数据，适配多种临床场景，可推广至其他逆问题
利用先验知识的正则化方法对解决电导断层成像（EIT）等不适定逆问题至关重要。然而，由于解剖结构复杂多变，设计有效正则化并整合先验信息仍具挑战。本文提出SDEIT，首个将大规模文生图模型Stable Diffusion 3.5应用于EIT的语义驱动框架。SDEIT采用自然语言提示作为语义先验，耦合隐式神经表示（INR）网络与即插即用优化方案，以SD生成图像作为生成先验，显著提升结构一致性并恢复精细细节。该方法不依赖配对训练数据，增强对多样化EIT场景的适应性。大量仿真与实验结果表明，SDEIT优于当前最先进方法，具备更高准确率与鲁棒性。本工作为将多模态先验引入不适定逆问题开辟新路径。
原文摘要 · Abstract (English)
Regularization methods using prior knowledge are essential in solving ill-posed inverse problems such as Electrical Impedance Tomography (EIT). However, designing effective regularization and integrating prior information into EIT remains challenging due to the complexity and variability of anatomical structures. In this work, we introduce SDEIT, a novel semantic-driven framework that integrates Stable Diffusion 3.5 into EIT, marking the first use of large-scale text-to-image generation models in EIT. SDEIT employs natural language prompts as semantic priors to guide the reconstruction process. By coupling an implicit neural representation (INR) network with a plug-and-play optimization scheme that leverages SD-generated images as generative priors, SDEIT improves structural consistency and recovers fine details. Importantly, this method does not rely on paired training datasets, increasing its adaptability to varied EIT scenarios. Extensive experiments on both simulated and experimental data demonstrate that SDEIT outperforms state-of-the-art techniques, offering superior accuracy and robustness. This work opens a new pathway for integrating multimodal priors into ill-posed inverse problems like EIT.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。