Non-invasive temperature prediction of in vitro therapeutic ultrasound signals using neural networks.
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 degrees 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.
Teixeira
C A
CA
Centre for Intelligent Systems, Faculty of Sciences and Technology, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal. cateixeira@ualg.pt
Ruano
A E
AE
Ruano
M Graça
MG
Pereira
W C A
WC
Negreira
C
C
D001831:Body Temperature; D006801:Humans; D006979:Hyperthermia, Induced; D008954:Models, Biological; D016571:Neural Networks, Computer; D014464:Ultrasonic Therapy