Loading...
8 results
Search Results
Now showing 1 - 8 of 8
- 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.
- 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.
- 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.
- 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.
- Characterization of temperature-dependent echo-shifts and backscattered energy induced by thermal ultrasoundPublication . Ruano, M. Graça; Teixeira, C. A.; Rahmati, Javid J.Existence of accurate temporal-spatial temperature models, which would enable non-invasive estimates, will promote ultrasound-based thermal therapy applications. These models should reflect the tissue temperature with a maximum absolute error of 0.5 ºC within 1 cm3. In-vitro experiments have been developed to evaluate the temperature variations induced by standard ultrasound therapeutic device emitting continuously on gel-based phantom and on pork meat tissue using three different emitting intensities (1, 1.5 and 2 W/cm3). Temperature estimates were performed based on raw RF data collected using a second ultrasound transducer (imaging transducer). This second transducer worked in pulse-echo mode, and was placed perpendicularly to the therapeutic transducer. In order to access the quality of the estimates, temperatures were acquired by five and by two thermocouples placed in the gel-based phantom and on the porcine sample, respectively. At every 10 seconds the temperature and one RF-line is stored in a PC for future processing. The possibility to estimate temperature was assessed by considering two RFline features: temporal echo-shifts produced by changes in speed-of-sound and medium expansion/contraction and by changes on the backscattered energy originated by medium inhomogeneities. On one hand, results prove that echo-shifts correlated with temperature in both types of medium (phantom and ex-vivo porcine muscle). On the other hand, analyzing the backscattered energies one may conclude that this measures correlates with temperature in the porcine sample and not on the phantom. This led us to conclude that the developed phantom is not appropriate for studying changes on backscattered energy with temperature. Energy analysis of the porcine sample confirms the non-uniform temperature variation due to the existence of a heterogeneous media with different sound propagation velocities.
- Noise cancellation technique for Doppler ultrasound spectrogram enhancementPublication . Zabihian, B.; Teixeira, C. A.; Ruano, M. GraçaDoppler ultrasound (DU) automatic detection of clinically relevant blood flow spectral parameters may be a difficult task when the spectrogram is corrupted with different types of noise. This paper introduces a technique to eliminate the spectrogram background noise without damaging the blood flow signals' content. Tests were performed on simulated signals with known spectral characteristics. Different signal to noise ratios were tested. Results of 100 signals were averaged. Based on the STFT spectrogram the center frequency and bandwidth of the simulated noisy signals were computed and evaluated against the deterministic curve. Results obtained by defining the background area of DU simulated signals and applying 7% of Cancellation level on signals with 10 dB of signal-to-noise ratio (SNR) produced mean frequency estimations with a maximum bias of 106 Hz. The maximum bias of the bandwidth estimations in this case was 124 Hz. Since the sampling frequency of the DU simulated signals were 12800 Hz, usage of the proposed noise cancelation technique enabled spectral parameters estimations with meaningless errors demonstrating the effectiveness of the proposed technique. © 2011 IEEE.