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- Soft-computing techniques applied to artificial tissue temperature estimationPublication . Teixeira, C. A.; Ruano, M. Graca; Ruano, A. E.Safety and efficiency of thermal therapies strongly rely on the ability to quantify temperature evolution in the treatment region. Research has been developed in this field, and both invasive and non-invasive technologies have been reported. Till now, only the magnetic resonance imaging (MRI) achieved the hyperthermia/diathermia gold standard value of temperature resolution of 0.5oC in 1cm3, in an in-vivo scenario. However, besides the cost of MRI technology, it does not enable a broad-range therapy application due to its complex environment. Alternatively, backscattered ultrasound (BSU) seems a promising tool for thermal therapy, but till now its performance was only quantitatively tested on homogeneous media and on single-intensity and three-point assessment have been reported. This thesis reports the research performed on the evaluation of time-spatialtemperature evolution based mainly on BSU signals within artificial tissues. Extensive operating conditions were tested on several experimental setups based on dedicated phantoms. Four and eight clinical ultrasound intensities, up to five spatial points, homogeneous and heterogeneous multi-layered phantoms were considered. Spectral and temporal temperature-dependent BSU features were extracted, and applied as invasive and non-invasive methodologies input information. Softcomputing methodologies have been used for temperature estimation. From linear iterative model structure models, to multi-objective genetic algorithms (MOGA) model structure optimisation for linear models, radial basis functions neural netxi xii works (RBFNNs), RBFNNs with linear inputs (RBFLICs), and for the adaptivenetwork- based fuzzy inference system (ANFIS) have been used to estimate the temperature induced on the phantoms. The MOGA+RBFNN methodology, fed with completely data-driven information, estimated temperature with maximum absolute errors less than 0.5oC within two spatial axes. The proposed MOGA+RBFNN methodology applied to non-invasive estimation on multi-layered media, is a innovative approach, as far as known, and enabled a step forward on the therapeutic temperature characterisation, motivating future instrumentation temperature control.
- A scalable and open source linear positioning system controllerPublication . Medeiros, M. C.; Fernandes, A. J. A.; Teixeira, C. A.; Ruano, M. GraçaThis paper is on the implementation of a dual axis positioning system controller. The system was designed to be used for space-dependent ultrasound signal acquisition problems, such as pressure field mapping. The work developed can be grouped in two main subjects: hardware and software. Each axis includes one stepper motor connected to a driver circuit, which is then connected to a processing unit. The graphical user interface is simple and clear for the user. The system resolution was computed as 127 mu m with an accuracy of 2.44 mu m. Although the target application is ultrasound signal acquisition, the controller can be applied to other devices that has up to four stepper motors. The application was developed as an open source software, thus it can be used or changed to fit different purposes.
- Métodos de soft computing para la estimación no invasiva de la temperatura en medios multicapa empleando ultrasonido retrodispersoPublication . Teixeira, C. A.; Pereira, W. C. A.; Ruano, M. Graça; Ruano, AntonioLa seguridad y eficacia de las terapias térmicas están ligadas con la determinación exacta de la temperatura, es por ello que la retroalimentacón de la temperatura en los métodos computacionales es de vital importancia.
- 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.
- Single black-box models for two-point non-invasive temperature predictionPublication . Teixeira, C. A.; Ruano, M. Graça; Ruano, Antonio; Pereira, W. C. A.; Negreira, C.In this paper the performance of a genetically selected radial basis functions neural network is evaluated for non-invasive two-point temperature estimation in a homogeneous medium, irradiated by therapeutic ultrasound at physiotherapeutic levels. In this work a single neural network was assigned to estimate the temperature profile at the two considered points, and more consistent results were obtained than when considering one model for each point. This result was possible by increasing the model complexity. The best model predicts the temperature from two unseen data sequences during approximately 2 hours, with a maximum absolute error less than 0.5 oC, as desired for a therapeutic temperature estimator.
- A data-driven methodology for non-invasive tempearture estimation in multilayered mediaPublication . Teixeira, C. A.; Pereira, W. C. A.; Ruano, M. Graça; Ruano, AntonioA main limitation on the application of thermal therapies is the lack of quantitative temperature knowledge in the treatment zone. This knowledge would increase the safety and effectiveness, through a correct instrumentation control. The portability and relative low cost of the ultrasound devices encourages the application of backscattered ultrasound (BSU) for temperature estimation. So far the methods based on BSU were only developed for homogeneous media. This paper reports a data-driven methodology able to estimate temperature in non-homogeneous media, with the required resolution. Appropriate generalisation performance was observed even under operating situations never considered in the training procedure.
- A soft-computing methodology for noninvasive time-spatial temperature estimationPublication . Teixeira, C. A.; Ruano, M. Graça; Ruano, Antonio; Pereira, W. C. A.The safe and effective application of thermal therapies is restricted due to lack of reliable noninvasive temperature estimators. In this paper, the temporal echo-shifts of backscattered ultrasound signals, collected from a gel-based phantom, were tracked and assigned with the past temperature values as radial basis functions neural networks input information. The phantom was heated using a piston-like therapeutic ultrasound transducer. The neural models were assigned to estimate the temperature at different intensities and points arranged across the therapeutic transducer radial line (60 mm apart from the transducer face). Model inputs, as well as the number of neurons were selected using the multiobjective genetic algorithm (MOGA). The best attained models present, in average, a maximum absolute error less than 0.5 C, which is pointed as the borderline between a reliable and an unreliable estimator in hyperthermia/diathermia. In order to test the spatial generalization capacity, the best models were tested using spatial points not yet assessed, and some of them presented a maximum absolute error inferior to 0.5 C, being “elected” as the best models. It should be also stressed that these best models present implementational low-complexity, as desired for real-time applications.
- 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.
- Noninvasive black-box temperature simulation: precise spatial generalisationPublication . Teixeira, C. A.; Ruano, Antonio; Ruano, M. Graça; Pereira, W. C. A.In this paper the performance of a blackbox methodology is accessed for non-invasive timespatial temperature estimation. A gel-based phantom was heated at different intensities with therapeutic ultrasound, while temperature and RF-lines were collected. The models were trained and its structure selected to estimate the temperature in three discrete points, and at the end validated in unseen data, in the trained points and in two additional intermediate untrained points, in order to test the model s spatial generalization capacity. The best model had low complexity and a high generalization capacity, presenting in both the points and intensities a maximum absolute error inferior to 0.5 ºC, as desired in hyperthermia/diathermia.
- Linear versus non-linear non-invasive temperature predictors in a homogeneous medium subjected to physiotherapeutic ultrasoundPublication . Teixeira, C. A.; Ruano, M. Graça; Pereira, W. C. A.; Ruano, Antonio; Negreira, C.The lack of accurate time-spatial temperature estimators/predictors conditions the safe application of thermal therapies, such as hyperthermia. In this paper, a comparison between a linear and a non-linear class of models for non-invasive temperature prediction in a homogeneous medium, subjected to ultrasound at physiotherapeutic levels is presented. The linear models used were autoregressive with exogenous inputs (ARX) and the non-linear models were radial basis functions neural networks (RBFNN). In order to create and validate the models, an experiment was build to extract in vitro ultrasound RF-lines, as well as its correspondent temperature values. Then, features were extracted from the measured RF-lines and the models were trained and validated. For both the models, the best-fitted structures were selected using the multi-objective genetic algorithm (MOGA), given the enormous number of possible structures. The best RBFNN model presented a maximum absolute predictive error in the validation set five times less than the value presented by the best ARX model. In this work, the best RBFNN reached a maximum absolute error of 0.42 ºC, which is bellow the value pointed as a borderline between an appropriate and an undesired temperature estimator, which is 0.5 ºC. The average error was one order of magnitude less in the RBFNN case, and a less biased estimation was met. In addition, the best RBFNN needed less environmental information (inputs), given the capacity to non-linearly relate the information. The results obtained are encouraging, considering that coherent results should be obtained in a time-spatial modelling schema using RBFNN models.
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