Abstract
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 multi-objective 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 non-invasive 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.
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Acknowledgements
The authors would like to thank the financial support of: Fundação para a Ciência e a Tecnologia (grant SFRH/BD/1461/2003), Portugal, and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq/CYTED/ 490.013/03-1), Brazil. The authors would also like to acknowledge Prof. Hector Gomez and M.Sc. Guillermo Cortela for their support in the real data collection.
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Teixeira, C.A., Ruano, A.E., Ruano, M.G. et al. Non-invasive temperature prediction of in vitro therapeutic ultrasound signals using neural networks. Med Bio Eng Comput 44, 111–116 (2006). https://doi.org/10.1007/s11517-005-0004-2
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DOI: https://doi.org/10.1007/s11517-005-0004-2