APPLICATION OF STOCHASTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR NONINVASIVE DETERMINATION OF TEMPERATURE-DEPENDENT THERMAL PROPERTIES OF BIOLOGICAL TISSUE

Abstract

The knowledge of thermal properties of biological tissue is of great importance to medical diagnosis and thermotherapy. In this study, an inverse model based on stochastic particle swarm optimization (SPSO) algorithm and sensitivity analysis is developed to determine the temperature-dependent thermal properties of one-dimensional biological tissue. The noninvasive determination task is formulated as an inverse problem which is solved by the SPSO algorithm from the knowledge of surface temperature measurements. A logistic mapping strategy is introduced to improve the quality of the initial particle swarm. Retrieval results demonstrate that the developed inverse technique is robust to determine the temperature-dependent thermal conductivity, specific heat and blood perfusion rate of biological tissue. The maximum relative error of the simultaneous determination results is less than 0.8%. The maximum determination error is 1.15% when a 3% random deviation is considered. Meanwhile, the present SPSO algorithm is proved to be more efficient and accurate than other intelligent algorithms. Considering the convergence accuracy and computational time, the population size of the SPSO algorithm is suggested to be M = 80.