An algorithm for fast reconstruction of electromagnetic tomography images

For the inverse problem of electromagnetic tomography (EMT), the pathological and ill posed problems of the sensitivity matrix are discussed. A new electromagnetic tomography image reconstruction algorithm is proposed for this situation. Firstly, the principal component analysis (PCA) is used to reduce the dimension of the sensitivity matrix, and then the singular value decomposition (SVD) is used to calculate the generalized inverse matrix to reconstruct the image. After the covariance matrix of the sensitivity matrix is obtained, we need to compute the number of eigenvalues that the covariance matrix should retain. Then the maximization of the image correlation coefficient algorithm is proposed to solve it by using the unique multi-sample characteristics of the sensitivity matrix. It is more reasonable for sensitivity matrix to remove redundant information. And it improves the stability of the solution as far as possible without losing imaging feature information. When the actual data is used for imaging, this algorithm needs only one matrix multiplication, which provides the possibility for fast real-time imaging. In conclusion, compared with the traditional single step algorithm and iterative algorithm, the proposed algorithm has obvious advantages in both imaging quality and speed. © 2018, Editorial Board of JBUAA. All right reserved.