Improving the image reconstruction in Electrical Impedance Tomography (EIT) with block matrix-based Multiple Regularization (BMMR): A practical phantom study

TLDR: In this paper, a block matrix based multiple regularization (BMMR) technique was used for conductivity image reconstruction in electrical impedance tomography (EIT) using practical phantoms.

Abstract: Conductivity image reconstruction is studied with a Block Matrix based Multiple Regularization (BMMR) technique in Electrical Impedance Tomography (EIT) using practical phantoms. The response matrix (JTJ) is partitioned into several sub-block matrices and the largest element of each sub-block matrices is taken as regularization parameter for the nodes of the FEM mesh contained by that sub-block. Boundary potential data are collected from practical phantoms with different inhomogeneity configurations and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm. Conductivity images, reconstructed with BMMR technique, are compared with the images obtained with Single-step Tikhonov Regularization (STR) and modified Levenberg-Marquardt Regularization (LMR) methods. Results show that BMMR technique reduces the reconstruction error and reconstruct the better conductivity images by improving the conductivity profile of the domain under test for all the phantoms. Image analysis showed that the BMMR method improves image contrast parameters, conductivity profiles, and spatial resolution of the reconstructed images.