Temperature models of a homogeneous medium under therapeutic ultrasound

Temperature modelling of human tissue subjected to ultrasound for therapeutic use is essential for an accurate instrumental assessment and calibration. The existence of accurate temperature models would enable a safe and efficient application of the thermal therapies. The main objective of this work is the comparison between the performance of non-linear models and linear models for punctual temperature estimation in a homogeneous medium. The final goal of the work hereby reported is the construction of neural models for “in-vivo” temperature estimation. The linear models employed were AutoRegressive with eXogenous inputs (ARX), and the non-linear models used were radial basis functions neural network (RBFNN). The best-performed RBFNN structures were selected using the Multi-objective Genetic Algorithm (MOGA). The best performed neural structure present a maximum absolute error of 0.2 oC, which is one order magnitude less than the one presented by the best ARX.