Fully tuned RBF neural network controller for ultrasound hyperthermia cancer tumour therapy

Thermal dose is an important clinical efficacy index for hyperthermia cancer treatment. This paper presents a new direct radial basis function (RBF) neural network controller for high-temperature hyperthermia thermal dose during the therapeutic procedure of cancer tumours by short-time pulses of high-intensity focused ultrasound (HIFU). The developed controller is stabilized and automatically tuned based on Lyapunov functions and ant colony optimization (ACO) algorithm, respectively. In addition, this thermal dose control system has been validated using one-dimensional (1-D) biothermal tissue model. Simulation results showed that the fully tuned RBF neural network controller outperforms other controllers in the previous studies by achieving targeted thermal dose with shortest treatment times less than 13.5 min, avoiding the tissue cavitation during the thermal therapy. Moreover, the maximum value of its mean integral time absolute error (MTAE) is 98.64, which is significantly less than the resulted errors for the manual-tuned controller under the same treatment conditions of all tested cases. In this study, integrated ACO method with robust RBF neural network controller provides a successful and improved performance to deliver accurate thermal dose of hyperthermia cancer tumour treatment using the focused ultrasound transducer without external cooling effect.