A High-Resolution Image Reconstruction Method of Lung Nodules With B-Spline-Based Whale Optimization Algorithm (B-WOA) for Electrical Impedance Tomography

A B-spline-based Whale Optimization Algorithm (B-WOA) is proposed to realize a high-resolution electrical impedance tomography (EIT) image reconstruction of lung nodules. The method achieves the mapping between the target position and the conductivity distribution by the B-spline curve and establishes the optimized objective function by the sensitivity matrix method, based on which the lung nodule image reconstruction is realized by the WOA. The quality of images reconstructed using WOA and three conventional algorithms is compared by numerical simulation in the randomly generated lung nodule model. The simulation results show that the average value of the image correlation coefficient (ICC) of the reconstructed images using the WOA is improved by 4.8% compared with the Tikhonov regularization algorithm, which performs best among the traditional algorithms. In addition, an experimental model of lung nodules was established to validate the proposed method. Biological tissues of different diameters were used to simulate the lung nodule region. The performance of the WOA, the Tikhonov regularization algorithm, and the GA were investigated in the dual-target imaging experiment. The experimental results show that the average ICC values of the reconstructed images using the WOA are improved by 4.7% and 3.5% over the Tikhonov regularization algorithm and the GA, respectively. Therefore, the proposed B-WOA can achieve high-resolution imaging of the distribution of tiny targets, which is expected to be used for the clinical detection of lung nodules.