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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.
- Influence of temperature variations on the entropy and correlation of the Grey-Level Co-occurrence Matrix from B-Mode imagesPublication . Alvarenga, A. V.; Teixeira, C. A.; Ruano, M. Graça; Pereira, W. C. A.In this work, the feasibility of texture parameters extracted from B-Mode images were explored in quantifying medium temperature variation. The goal is to understand how parameters obtained from the gray-level content can be used to improve the actual state-of-the-art methods for non-invasive temperature estimation (NITE). B-Mode images were collected from a tissue mimic phantom heated in a water bath. The phantom is a mixture of water, glycerin, agar-agar and graphite powder. This mixture aims to have similar acoustical properties to in vivo muscle. Images from the phantom were collected using an ultrasound system that has a mechanical sector transducer working at 3.5 MHz. Three temperature curves were collected, and variations between 27 and 44 degrees C during 60 min were allowed. Two parameters (correlation and entropy) were determined from Grey-Level Co-occurrence Matrix (GLCM) extracted from image, and then assessed for non-invasive temperature estimation. Entropy values were capable of identifying variations of 2.0 degrees C. Besides, it was possible to quantify variations from normal human body temperature (37 degrees C) to critical values, as 41 degrees C. In contrast, despite correlation parameter values (obtained from GLCM) presented a correlation coefficient of 0.84 with temperature variation, the high dispersion of values limited the temperature assessment. (C) 2009 Elsevier B.V. All rights reserved.
- Tissue temperature estimation with pulse-echo in blood flow presencePublication . Ruano, M. Graça; Duarte, H. Simões; Teixeira, C. A.Aiming at time-spatial characterization of tissue temperature when ultrasound is applied for thermal therapeutic proposes two experiments were developed considering gel-based phantoms, one of them including an artificial blood vessel. The blood vessel was mimicking blood flow in a common carotid artery. For each experiment phantoms were heated by a therapeutic ultrasound (TU) device emitting different intensities (0.5, 1, 1.5, 1.8 W/cm2). Temperature was monitored by thermocouples and estimated through imaging ultrasound transducer's signals within specific special points inside the phantom. The temperature estimation procedure was based on temporal echo-shifts (TES), computed based on echo-shifts collected through image ultrasound (IU) transducer. Results show that TES is a reliable non-invasive method of temperature estimation, regardless the TU intensities applied. Presence of a pulsatile blood flow vessel in the focal point of TU transducer reduces thermal variation in more than 50%, also affecting the temperature variation in the surrounding area. In other words, vascularized tissues require longer ultrasound thermal therapeutic sessions or higher TU intensities and inclusion of IU in the therapeutic procedure enables non-invasive monitoring of temperature. © 2013 IEEE.
- On the assessment of time-shift variations from backscattered ultrasound for large temperature changes in biological phantomsPublication . Teixeira, C. A.; Ruano, M. Graça; Pereira, W. C. A.; Garreton, L. G.This work reports the assessment of time-shifts (TS) from backscattered ultrasound (BSU) signals when large temperature variations (up to 15 degrees C) were induced in a gel-based phantom. The results showed that during cooling temperature is linear with TS at a rate of approximately 74 ns/degrees C. However during a complete heating/cooling cycle, the relation is highly non-linear. This can be explained by the fact that during cooling the temperature distribution is more uniform. Another problem to report is that TS is very sensitive to external movements.
- Generalization assessment of non-invasive black-box temperature estimators from therapeutic ultrasoundPublication . Teixeira, C. A.; Ruano, Antonio; Ruano, M. Graça; Pereira, W. C. A.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.