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Generalization assessment of non-invasive black-box temperature estimators from therapeutic ultrasound
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Orientador(es)
Resumo(s)
The objective of this work is the generalisation performance assessment, in terms of intensity, of non-invasive temperature models based on radial basis functions neural networks. The models were built considering data collected at three
therapeutic ultrasound intensities, (among 0.5, 1.0, 1.5 and 2.0 W/cm2) and then were validated in fresh data, which contain information from the trained intensities and form the untrained intensity. The models were built to estimate the temperature evolution (during 35 min) in a gel-based
phantom, heated by physiotherapeutic ultrasound at four different intensities. It was found that the best models built
without data from the intermediate intensities (0.5, 1.0 and 1.5 W/cm2) perform well in validation at all the intensities.
On the other hand, the models built without data from the extrapolated intensity (2,0 W/cm2) presented unsatisfactory results in validation. This is because the models parameters were found considering a space bounded by the data used in their construction, and then the application of data outside this space resulted in poor performance. The models build without the intermediate data, for the three considered points, presented a maximum absolute error inferior to 0.5 ºC (which is accepted for therapeutic applications). The best models
also presented a low computational complexity, as desired for real-time applications.
Descrição
Palavras-chave
Non-invasive temperature estimation Data-driven models Radial basis functions neural networks Multi-objective genetic algorithms Ultrasound Physiotherapy
Contexto Educativo
Citação
Teixeira, C. A.; Ruano, A. E.; Ruano, M. G.; Pereira, W.C . A. Generalization assessment of non-invasive black-box temperature estimators from therapeutic ultrasound, Revista Brasileira de Engenharia Biomédica, 23, 2, 143-151, 2007.