用扩散模型生成先验，提升电导率成像重建精度
Diff-INR: Generative Regularization for Electrical Impedance Tomography
- 将扩散模型作为生成先验，结合隐式神经表示优化重建
- 仿真与实验数据均达当前最优，对网格密度不敏感
- 适合医学成像、逆问题求解等需要高鲁棒性的场景
电导率断层成像（EIT）是一种非侵入式成像技术，通过边界测量重建体内电导率分布。然而，其重建面临严重不适定的非线性逆问题，难以获得准确结果。为此，我们提出Diff-INR，一种将生成正则化与隐式神经表示（INR）结合的新型方法，通过扩散模型引入几何先验，有效克服传统正则化方法的局限。通过整合预训练扩散正则化器与INR，该方法在仿真和实验数据上均实现了最先进的重建精度。方法在不同网格密度和超参数设置下表现出强鲁棒性，展现出良好灵活性与高效性。该进展显著提升了对EIT不适定性的处理能力。此外，其原理可推广至其他存在类似不适定逆问题的成像模态。
原文摘要 · Abstract (English)
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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