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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.
- Evaluation of the influence of large temperature variations on the grey level content of B-mode imagesPublication . Alvarenga, A. V.; Teixeira, César; Ruano, M. Graça; Pereira, WagnerIn this work, the variation of the grey-level content of B-Mode images is assessed, when the medium is subjected to large temperature variations. The goal is to understand how the features obtained from the grey-level pattern can be used to improve the actual state-of-the-art methods for non-invasive temperature estimation (NITE). Herein, B-Mode images were collected from a tissue mimic phantom heated in a water bath. Entropy was extracted from image Grey-Level Co-occurrence Matrix, and then assessed for non-invasive temperature estimation. During the heating period, the average temperature varies from 27oC to 44oC, and entropy values were capable of identifying variations of 2.0oC. Besides, it was possible to quantify variations in the range from normal human body temperature (37oC) to critical values, as 41oC. Results are promising and encourage us to study the uncertainty associated to the experiment trying to improve the parameter sensibility.
- 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.
- On the possibility of non-invasive multilayer temperature estimation using soft-computing methodsPublication . Teixeira, C. A.; Pereira, W. C. A.; Ruano, Antonio; Ruano, M. GraçaObjective and motivation: This work reports original results on the possibility of non-invasive temperature estimation (NITE) in a multilayered phantom by applying soft-computing methods. The existence of reliable non-invasive temperature estimator models would improve the security and efficacy of thermal therapies. These points would lead to a broader acceptance of this kind of therapies. Several approaches based on medical imaging technologies were proposed, magnetic resonance imaging (MRI) being appointed as the only one to achieve the acceptable temperature resolutions for hyperthermia purposes. However, MRI intrinsic characteristics e.g., high instrumentation cost) lead us to use backscattered ultrasound (BSU). Among the different BSU features, temporal echo-shifts have received a major attention. These shifts are due to changes of speed-of-sound and expansion of the medium. Novelty aspects: The originality of this work involves two aspects: the estimator model itself is original (based on soft-computing methods) and the application to temperature estimation in a three-layer phantom is also not reported in literature. Materials and methods: In this work a three-layer (non-homogeneous) phantom was developed. The two external layers were composed of (in % of weight): 86.5% degassed water, 11% glycerin and 2.5% agar– agar. The intermediate layer was obtained by adding graphite powder in the amount of 2% of the water weight to the above composition. The phantom was developed to have attenuation and speed-of-sound similar to in vivo muscle, according to the literature. BSU signals were collected and cumulative temporal echo-shifts computed. These shifts and the past temperature values were then considered as possible estimators inputs. A soft-computing methodology was applied to look for appropriate multilayered temperature estimators. The methodology involves radial-basis functions neural networks (RBFNN) with structure optimized by the multi-objective genetic algorithm (MOGA). In this work 40 operating conditions were considered, i.e. five 5-mm spaced spatial points and eight therapeutic intensities ðISATAÞ: 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7 and 2:0W=cm2. Models were trained and selected to estimate temperature at only four intensities, then during the validation phase, the best-fitted models were analyzed in data collected at the eight intensities. This procedure leads to a more realistic evaluation of the generalisation level of the best-obtained structures. Results and discussion: At the end of the identification phase, 82 (preferable) estimator models were achieved. The majority of them present an average maximum absolute error (MAE) inferior to 0.5 C. The best-fitted estimator presents a MAE of only 0.4 C for both the 40 operating conditions. This means that the gold-standard maximum error (0.5 C) pointed for hyperthermia was fulfilled independently of the intensity and spatial position considered, showing the improved generalisation capacity of the identified estimator models. As the majority of the preferable estimator models, the best one presents 6 inputs and 11 neurons. In addition to the appropriate error performance, the estimator models present also a reduced computational complexity and then the possibility to be applied in real-time.
- 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.
- On the possibility of non-invasive multilayer temperature estimation using soft-computing methodsPublication . Teixeira, C. A.; Pereira, W. C. A.; Ruano, Antonio; Ruano, M. GraçaObjective and motivation: This work reports original results on the possibility of non-invasive temperature estimation (NITE) in a multilayered phantom by applying soft-computing methods. The existence of reliable non-invasive temperature estimator models would improve the security and efficacy of thermal therapies. These points would lead to a broader acceptance of this kind of therapies. Several approaches based on medical imaging technologies were proposed, magnetic resonance imaging (MRI) being appointed as the only one to achieve the acceptable temperature resolutions for hyperthermia purposes. However, MRI intrinsic characteristics (e. g., high instrumentation cost) lead us to use backscattered ultrasound (BSU). Among the different BSU features, temporal echo-shifts have received a major attention. These shifts are due to changes of speed-of-sound and expansion of the medium. Novelty aspects: The originality of this work involves two aspects: the estimator model itself is original (based on soft-computing methods) and the application to temperature estimation in a three-layer phantom is also not reported in literature. Materials and methods: In this work a three-layer (non-homogeneous) phantom was developed. The two external layers were composed of (in % of weight): 86.5% degassed water, 11% glycerin and 2.5% agar agar. The intermediate layer was obtained by adding graphite powder in the amount of 2% of the water weight to the above composition. The phantom was developed to have attenuation and speed-of-sound similar to in vivo muscle, according to the literature. BSU signals were collected and cumulative temporal echo-shifts computed. These shifts and the past temperature values were then considered as possible estimators inputs. A soft-computing methodology was applied to look for appropriate multilayered temperature estimators. The methodology involves radial-basis functions neural networks (RBFNN) with structure optimized by the multi-objective genetic algorithm (MOGA). In this work 40 operating conditions were considered, i.e. five 5-mm spaced spatial points and eight therapeutic intensities (I(SATA)): 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7 and 2:0 W/cm(2). Models were trained and selected to estimate temperature at only four intensities, then during the validation phase, the best-fitted models were analyzed in data collected at the eight intensities. This procedure leads to a more realistic evaluation of the generalisation level of the best-obtained structures. Results and discussion: At the end of the identification phase, 82 (preferable) estimator models were achieved. The majority of them present an average maximum absolute error (MAE) inferior to 0.5 degrees C. The best-fitted estimator presents a MAE of only 0.4 degrees C for both the 40 operating conditions. This means that the gold-standard maximum error (0.5 degrees C) pointed for hyperthermia was fulfilled independently of the intensity and spatial position considered, showing the improved generalisation capacity of the identified estimator models. As the majority of the preferable estimator models, the best one presents 6 inputs and 11 neurons. In addition to the appropriate error performance, the estimator models present also a reduced computational complexity and then the possibility to be applied in real-time. Conclusions: A non-invasive temperature estimation model, based on soft-computing technique, was proposed for a three-layered phantom. The best-achieved estimator models presented an appropriate error performance regardless of the spatial point considered (inside or at the interface of the layers) and of the intensity applied. Other methodologies published so far, estimate temperature only in homogeneous media. The main drawback of the proposed methodology is the necessity of a-priory knowledge of the temperature behavior. Data used for training and optimisation should be representative, i.e., they should cover all possible physical situations of the estimation environment. (C) 2009 Elsevier B.V. All rights reserved.
- 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.