Reconstruction of conductivity distribution with a compound variational strategy in electrical impedance tomography

摘要

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 L-1 algorithm transforms the L-p-norm to an L-1-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.