Diff-INR: Generative Regularization for Electrical Impedance Tomography
<|reference_start|>Diff-INR: Generative Regularization for Electrical Impedance Tomography: 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.<|reference_end|>
@article{tong2024diff-inr:,
    title={Diff-INR: Generative Regularization for Electrical Impedance Tomography},
    author={Bowen Tong, Junwu Wang, Dong Liu},
    journal={arXiv preprint arXiv:2409.04494},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04494},
    primaryClass={eess.IV cs.CV}
}