Diff-INR: Generative Regularization for Electrical Impedance Tomography
This addresses the challenge of accurate conductivity reconstruction in EIT for medical or industrial imaging, with potential applicability to other imaging modalities, though it appears incremental as it builds on existing techniques like diffusion models and INRs.
The paper tackles the ill-posed nonlinear inverse problem in Electrical Impedance Tomography (EIT) by proposing Diff-INR, a method combining generative regularization with Implicit Neural Representations via a diffusion model, achieving state-of-the-art reconstruction accuracy in simulations and experiments.
Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that reconstructs conductivity distributions within a body from boundary measurements. However, EIT reconstruction is hindered by its ill-posed nonlinear inverse problem, which complicates accurate results. To tackle this, we propose Diff-INR, a novel method that combines generative regularization with Implicit Neural Representations (INR) through a diffusion model. Diff-INR introduces geometric priors to guide the reconstruction, effectively addressing the shortcomings of traditional regularization methods. By integrating a pre-trained diffusion regularizer with INR, our approach achieves state-of-the-art reconstruction accuracy in both simulation and experimental data. The method demonstrates robust performance across various mesh densities and hyperparameter settings, highlighting its flexibility and efficiency. This advancement represents a significant improvement in managing the ill-posed nature of EIT. Furthermore, the method's principles are applicable to other imaging modalities facing similar challenges with ill-posed inverse problems.