The regularized D-bar method is a popular method for solving Electrical Impedance Tomography (EIT) problems due to its efficiency and simplicity. It utilizes the low-pass truncated scattering data in the non-linear Fourier domain to solve the associated D-bar integral equations, yielding a smooth conductivity approximation. However, the D-bar reconstruction often presents low contrast and resolution due to the absence of accurate high-frequency information and the ill-posedness of the problem. In this paper, we propose a deep learning-based supervised approach for real-time EIT reconstruction. Based on the D-bar method, we propose to utilize both multi-scale frequency enhancement and spatial consistency for a high image quality reconstruction. Additionally, we propose a fixed-point iteration for solving discrete D-bar systems on GPUs for fast computation. Numerical results are performed for both the continuum model and complete electrode model simulation on KIT4 and ACT4 datasets to demonstrate notable improvements in absolute EIT imaging quality.

翻译：正则化D-bar方法是求解电阻抗断层成像问题的常用方法，因其高效性与简洁性而备受青睐。该方法通过在非线性傅里叶域中使用低通截断散射数据来求解相关的D-bar积分方程，从而获得平滑的电导率近似解。然而，由于缺乏准确的高频信息以及问题本身的不适定性，D-bar重建结果往往呈现较低的对比度与分辨率。本文提出一种基于深度学习的监督式方法用于实时EIT重建。在D-bar方法的基础上，我们提出同时利用多尺度频率增强与空间一致性来实现高质量图像重建。此外，我们提出一种在GPU上求解离散D-bar系统的定点迭代法以加速计算。通过在KIT4和ACT4数据集上对连续介质模型与完整电极模型进行数值实验，结果表明该方法在绝对EIT成像质量方面取得了显著提升。