As a potential imaging technique, electrical impedance tomography (EIT) is advantageous for reconstructing conductivity distribution. However, due to insufficient measurement, visualization is inevitably an ill-posed inverse problem. Moreover, reconstruction quality is affected by noise. To address these challenges, a novel variational model with an L_p-norm as fidelity and a hybrid total variation as penalty (L_p-HTV) is proposed for conductivity distribution reconstruction. Iterative reweighted L₁ algorithm transforms the L_p-norm to an L₁-norm and alternating direction method of multipliers is then utilized to solve objective function. Meanwhile, region of interest is defined to enhance robustness to noise and reduce staircase artifact. The performance of the proposed compound method is validated by several cases. Phantom experiments are also conducted. Compared with classic regularization methods, it is found that images reconstructed by the proposed method show large improvement. The results demonstrate that the proposed strategy is more competitive in visualizing conductivity distribution.

中文翻译：

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电阻抗层析成像中复合变分策略重建电导率分布

作为一种潜在的成像技术，电阻抗断层扫描 (EIT) 有利于重建电导率分布。然而，由于测量不足，可视化不可避免地是一个不适定的逆问题。此外，重建质量受噪声影响。为了应对这些挑战，提出了一种新的变分模型，其中 L _{p -}范数作为保真度，混合总变差作为惩罚 (L _p -HTV) 用于电导率分布重建。迭代重加权 L ₁算法将 L _p范数转换为 L ₁然后利用乘数的范数和交替方向方法来求解目标函数。同时，定义感兴趣区域以增强对噪声的鲁棒性并减少楼梯伪影。通过几个案例验证了所提出的复合方法的性能。还进行了幻像实验。与经典的正则化方法相比，该方法重建的图像显示出较大的改进。结果表明，所提出的策略在可视化电导率分布方面更具竞争力。