A novel near field radiation shaping technique by using data driven surrogate based optimization for nondestructive hyperthermia

Abstract

Herein, a novel and computationally efficient near field shaping technique for nondestructive hyperthermia applications is presented. The aim of nondestructive hyperthermia studies is to treat cancer cells in the body without damaging the healthy cells using the focused near-field characteristic of the antenna array. However, optimal near field shaping requires an optimization process for having accurately focus the energy into the desired coordinates, which is, a computational expensive and inefficient task due to the nature of full-wave electromagnetic (EM) analysis. To enable computationally efficient, and accurate near field shape optimization, a surrogate model is required. To achieve this, the near field radiation of the designed horn antenna array in free space is obtained by using a fast and reliable technique, analytical regularization method (ARM). After that, by using Artificial Neural Network based regression algorithm, a data driven surrogate model is created using the data samples generated by ARM. By this means, near field shaping optimization for focusing the energy to the region where the tumor is located can be achieved, as a multi-objective optimization problem to be solved via data driven surrogate model assisted optimization techniques. The performance results of K-fold validation and hold-out data sets for proposed data driven surrogate model are calculated using Mean Absolute Error metric which are obtained as 0.9 x 10(-3) and 1.4 x 10(-3) respectively. By observing the presented study case scenario, after 10 min of illumination with antenna array with optimally selected configurations, the targeted area (position of tumor) had reached to the critical temperature of 43 Celsius. Thus, it can be said that the proposed method is an efficient method, both in terms of (1) destroying tumor cells by heating without damaging the healthy cells, and (2) being a computationally efficient method.