Diff-INR：用于电阻抗断层成像的生成式正则化
Diff-INR: Generative Regularization for Electrical Impedance Tomography
AI总结：
针对EIT重建中的病态非线性逆问题，提出Diff-INR，利用扩散模型生成式正则化与INR及几何先验指导重建，在仿真和实验数据上取得最先进精度。
摘要：
电阻抗断层成像（Electrical Impedance Tomography，EIT）是一种非侵入式成像技术，可根据边界测量值重建人体内部的电导率分布。然而，EIT重建受到其病态非线性逆问题的阻碍，使获得准确结果变得复杂。为解决这一问题，我们提出 Diff-INR，这是一种通过扩散模型将生成式正则化与隐式神经表示（Implicit Neural Representations，INR）相结合的新方法。Diff-INR引入几何先验来指导重建，有效克服了传统正则化方法的不足。通过将预训练扩散正则化器与INR集成，我们的方法在仿真和实验数据上均实现了最先进的重建精度。该方法在不同网格密度和超参数设置下均表现出稳健性能，凸显了其灵活性和效率。这一进展标志着在处理EIT病态性质方面的显著改进。此外，该方法的原理还适用于其他面临类似病态逆问题挑战的成像模态。
英文摘要：
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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