Diff-INR: Generative Regularization for Electrical Impedance Tomography

• URL: http://arxiv.org/abs/2409.04494v2
• Date: Tue, 10 Sep 2024 07:40:06 GMT
• Title: Diff-INR: Generative Regularization for Electrical Impedance Tomography
• Authors: Bowen Tong, Junwu Wang, Dong Liu,
• Abstract summary: Electrical Impedance Tomography (EIT) reconstructs conductivity distributions within a body from boundary measurements. EIT reconstruction is hindered by its ill-posed nonlinear inverse problem, which complicates accurate results. We propose Diff-INR, a novel method that combines generative regularization with Implicit Neural Representations (INR) through a diffusion model.
• Score: 6.7667436349597985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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