Three-Dimensional Image Reconstruction of Breast Tumor by Electrical Impedance Tomography Based on Dimensional Grey Wolf Optimization Algorithm

A 3-D electrical impedance tomography (EIT) method based on dimensional grey wolf optimization (DGWO) algorithm is proposed for reconstructing 3-D images of breast tumors, which overcomes the inherent defects of 3-D EIT with high shape sensitivity and position sensitivity. In this study, a combination of multiple ellipsoids is used to mimic the shape of a breast tumor. The EIT problem is transformed into an optimization problem, which is solved by constructing an objective function and employing a sensitivity matrix approach. Finally, the optimization problem is solved using DGWO algorithm. In the simulation, a 16-electrode tapered sensor is used to randomly generate a tumor model and compared with the Tikhonov regularization algorithm and the combination of Tikhonov and NOSER regularization algorithm (TK-NOSER) to validate the performance of the proposed algorithm. Simulation results show that the DGWO algorithm differs little from the traditional regularization algorithm when the target is located in the center, while the advantage of the DGWO algorithm is more obvious when the target is located at the edge and non-spherical. DGWO algorithm improves the image correlation coefficient ( ${I} _{C}$ ) and image error ( ${I} _{E}$ ) by 12.20% and 24.00%, respectively, when the target is at the edge position. In the experiment, water and agar models of specific conductivity were used to simulate mammary glands and tumors. The experimental results show that the DGWO algorithm is superior to the TK-NOSER algorithm in terms of imaging accuracy and imaging artifacts, in which the average ${I} _{C}$ and average ${I} _{E}$ of the DGWO algorithm are improved by 19.14% and 15.12%, respectively. Therefore, the constructed experimental platform and the proposed algorithm achieve accurate imaging of target size and shape, which is expected to be applied to the clinical detection of breast cancer.