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- Temperature models of a homogeneous medium under therapeutic ultrasoundPublication . Teixeira, C. A.; Cortela, G.; Gomez, H.; Ruano, M. Graça; Ruano, Antonio; Negreira, C.; Pereira, W. C. A.Temperature modelling of human tissue subjected to ultrasound for therapeutic use is essential for an accurate instrumental assessment and calibration. Prior studies developed on a homogeneous medium are hereby reported. Non-linear punctual temperature modelling is proposed by means of Radial Basis Functions Neural Network (RBFNN) structures. The best-performed structures are obtained using a Multiobjective Genetic Algorithm (MOGA). The best performed neural structure presents a Root Mean Square Error (RMSE) of one order magnitude less than the one presented by the best behaved linear model - the AutoRegressive with eXogenous inputs (ARX); The maximum absolute error achieved with the neural model was 0.2 ºC.
- Linear versus non-linear non-invasive temperature predictors in a homogeneous medium subjected to physiotherapeutic ultrasoundPublication . Teixeira, C. A.; Ruano, M. Graça; Pereira, W. C. A.; Ruano, Antonio; Negreira, C.The lack of accurate time-spatial temperature estimators/predictors conditions the safe application of thermal therapies, such as hyperthermia. In this paper, a comparison between a linear and a non-linear class of models for non-invasive temperature prediction in a homogeneous medium, subjected to ultrasound at physiotherapeutic levels is presented. The linear models used were autoregressive with exogenous inputs (ARX) and the non-linear models were radial basis functions neural networks (RBFNN). In order to create and validate the models, an experiment was build to extract in vitro ultrasound RF-lines, as well as its correspondent temperature values. Then, features were extracted from the measured RF-lines and the models were trained and validated. For both the models, the best-fitted structures were selected using the multi-objective genetic algorithm (MOGA), given the enormous number of possible structures. The best RBFNN model presented a maximum absolute predictive error in the validation set five times less than the value presented by the best ARX model. In this work, the best RBFNN reached a maximum absolute error of 0.42 ºC, which is bellow the value pointed as a borderline between an appropriate and an undesired temperature estimator, which is 0.5 ºC. The average error was one order of magnitude less in the RBFNN case, and a less biased estimation was met. In addition, the best RBFNN needed less environmental information (inputs), given the capacity to non-linearly relate the information. The results obtained are encouraging, considering that coherent results should be obtained in a time-spatial modelling schema using RBFNN models.