摘要
Electrical impedance tomography (EIT) is a fast and cost-effective technique that provides a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT image reconstruction problem and the ill-posed linear inverse problem. First, we use block-based sampling for a large number of measured data from many electrodes. This method will reduce the size of Jacobian matrix and can improve accuracy of reconstruction by using more electrodes. And then, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Finally, we built up the relationship between compressed sensing and EIT definitely and induce the CS: two-step Iterative Shrinkage/Thresholding and block-based method into EIT image reconstruction algorithm. The results show that block-based compressed sensing enables the large scale 3D EIT problem to be efficient. For a 72-electrodes EIT system, our proposed method could save at least 61% of memory and reduce time by 72% than compressed sensing method only. The improvements will be obvious by using more electrodes. And this method is not only better at anti-noise, but also faster and better resolution.
Electrical impedance tomography (EIT) is a fast and cost-effective technique that provides a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT image reconstruction problem and the ill-posed linear inverse problem. First, we use block-based sampling for a large number of measured data from many electrodes. This method will reduce the size of Jacobian matrix and can improve accuracy of reconstruction by using more electrodes. And then, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Finally, we built up the relationship between compressed sensing and EIT definitely and induce the CS: two-step Iterative Shrinkage/Thresholding and block-based method into EIT image reconstruction algorithm. The results show that block-based compressed sensing enables the large scale 3D EIT problem to be efficient. For a 72-electrodes EIT system, our proposed method could save at least 61% of memory and reduce time by 72% than compressed sensing method only. The improvements will be obvious by using more electrodes. And this method is not only better at anti-noise, but also faster and better resolution.
