A neural controller for online laser power adjustment during the heat therapy process in the presence of...

The present research evaluated the efficiency of a control approach to control the temperature of a breast tumor mass in the presence of nanoparticles exposed to laser radiation. However, if the radiation is carried out in open loop manner it may result in excessive temperature rise healthy cells that exist in the vicinity of tumor's cells. This may lead to the death of healthy cells. So, using closed loop control methods is necessary to guarantee the preservation of healthy cells during the period of radiation. Therefore, in this study, an artificial neural network was trained as a controller. In other words, the trained neural network adjusted the laser power over a period of time in such a way that the temperature in the center of the tumor reached the desired level with an appropriate temporal behavior. The difference between the real temperature of the tumor and the desired temperature of it is the controller input, while the controller output determined the amount of laser power. The simulation studies were carried out using an appropriate physiological model in the presence of nanoparticles. First, Schrödinger equations were solved followed by the effective mass equation. Afterward the optimum number of nanoparticles to be used in the IR field was calculated. Next, the important electro-optical features related to the nanostructure, such as the absorption continuum and reflection continuum had been calculated. The neural network proposed controller was then evaluated through other simulation studies in the tumor mass model. The results showed a promising performance by the trained artificial neural network in adjusting radiated laser power for the desired temperature increase in the center of a tumor mass.