SDEIT: Semantic-Driven Electrical Impedance Tomography
This work addresses the problem of improving image reconstruction in EIT for medical or industrial applications by integrating multimodal priors, representing a novel paradigm shift rather than an incremental improvement.
The authors tackled the challenge of designing effective regularization for Electrical Impedance Tomography (EIT) by introducing SDEIT, a semantic-driven framework that integrates Stable Diffusion 3.5 to use natural language prompts as semantic priors, which outperformed state-of-the-art techniques in accuracy and robustness on simulated and experimental data.
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.