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- Non-invasive temperature prediction of in vitro therapeutic ultrasound signals using neural networksPublication . Teixeira, C. A.; Ruano, Antonio; Ruano, M. Graça; Pereira, W. C. A.; Negreira, C.In this paper, a novel black-box modelling scheme applied to non-invasive temperature prediction in a homogeneous medium subjected to therapeutic ultrasound is presented. It is assumed that the temperature in a point of the medium is non-linearly related to some spectral features and one temporal feature, extracted from the collected RF-lines. The black-box models used are radial basis functions neural networks (RBFNNs), where the best-fitted models were selected from the space of model structures using a genetic multiobjective strategy. The best-fitted predictive model presents a maximum absolute error less than 0.4 C in a prediction horizon of approximately 2 h, in an unseen data sequence. This work demonstrates that this type of black-box model is well-suited for punctual and noninvasive temperature estimation, achieving, for a single point estimation, better results than the ones presented in the literature, encouraging research on multi-point non-invasive temperature estimation.
- 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.