M. Graça Ruano

Real-time implementation of a Doppler signal spectral estimator using sequential and parallel processing techniques

Doppler signal spectral estimation has been used to evaluate blood flow parameters in order to diagnose cardiovascular diseases. The modified covariance (MC) method has proved to provide accurate estimation of the two spectral parameters employed in clinical diagnosis, namely mean frequency and bandwidth. The aim of the work reported in this paper is to determine an efficient real-time implementation of the MC spectral estimator by investigating several architectures and implementation methods. A comparative performance analysis of the implementation of the MC algorithm on several homogeneous and heterogeneous architectures incorporating transputers, digital signal processing (DSP) devices and a vector processor is reported. The performances of these architectures are evaluated and compared in terms of computational time (execution and communication) and gradient measurements. Analysis of the results reveals that both the homogeneous and heterogeneous DSP-based parallel architectures meet the real-time requirements.

On the possibility of non-invasive multilayer temperature estimation using soft-computing methods

OBJECTIVE 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 2.0W/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.

Non-invasive tissue temperature evaluation during application of therapeutic ultrasound: precise time-spatial non-linear modelling

The potential of thermal therapy’s applications improve with the development of accurate non-invasive time-spatial temperature models. These models should represent the non-linear tissue thermal behaviour and be capable of tracking temperature at both time-instant and spatial point. An in-vitro experiment was developed based on a gel phantom, heated by a therapeutic ultrasound (TUS) device emitting continuously. The heating process was monitored by an imaging ultrasound (IUS) transducer working in pulse-echo mode, placed perpendicularly to the TUS transducer. The IUS RF-lines and temperature values were collected 60 mm distant from the TUS transducer face. Three thermocouples were aligned along the IUS transducer axial direction and across the TUS transducer radial direction (1 cm spaced). Three different TUS intensities were applied. The non-invasive time-spatial evolutionary temperature models were created making use of radial basis functions neural networks (RBFNN). The neural network input information was: the propagation time-delay between RF-line echoes and the past temperature lags from three different medium locations and three different TUS intensities. A total of nine different operating situations were studied. The best RBFNN structures were automatically determined by a multiobjective genetic algorithm, due to the enormous number of possible structures. The RBFNN temperature models were evaluated with data never used in the models, neither at the training or structural selection phases. In order to precisely evaluate the model generalisation performance these data included the nine possible operating situations. The best model presents a maximum absolute error less than 0.5 degrees Celsius (gold-standard value for hyperthermia/diathermia applications). To be mentioned also that the best model presents low computational complexity enabling future real-time implementations. Concluding, a maximum absolute error below the gold-standard value pointed for hyperthermia/diathermia applications was attained. In addition, this methodology does not require a-priori determination of physical constants and mathematical simplifications required for analytical methodologies.

Neuro-genetic non-invasive temperature estimation: Intensity and spatial prediction

OBJECTIVES The existence of proper non-invasive temperature estimators is an essential aspect when thermal therapy applications are envisaged. These estimators must be good predictors to enable temperature estimation at different operational situations, providing better control of the therapeutic instrumentation. In this work, radial basis functions artificial neural networks were constructed to access temperature evolution on an ultrasound insonated medium. METHODS The employed models were radial basis functions neural networks with external dynamics induced by their inputs. Both the most suited set of model inputs and number of neurons in the network were found using the multi-objective genetic algorithm. The neural models were validated in two situations: the operating ones, as used in the construction of the network; and in 11 unseen situations. The new data addressed two new spatial locations and a new intensity level, assessing the intensity and space prediction capacity of the proposed model. RESULTS Good performance was obtained during the validation process both in terms of the spatial points considered and whenever the new intensity level was within the range of applied intensities. A maximum absolute error of 0.5 degrees C+/-10% (0.5 degrees C is the gold-standard threshold in hyperthermia/diathermia) was attained with low computationally complex models. CONCLUSION The results confirm that the proposed neuro-genetic approach enables foreseeing temperature propagation, in connection to intensity and space parameters, thus enabling the assessment of different operating situations with proper temperature resolution.

Time-Spatial Ultrasound Induced Temperature Evaluation on Perfused Phantoms

Ultrasound usage as a hyperthermia therapeutic procedure still requires more research on spatial-temporal temperature propagation characterization over tissues and on non-invasive temperature monitoring techniques. Present work compares how temperature is propagated inside tissue phantoms without and with blood vessels. The artificial vessels simulated common carotid and right hepatic arteries pulsatile blood flow. Phantoms were heated by a therapeutic ultrasound (TU) device emitting continuously and temperature was monitored by thermocouples at some specific spatial points and evaluated by analysis of imaging ultrasound (IU) transducer’s signals at those points. Experiments show that the existence of a blood vessel at the TU’s axial line reduces the tissue temperature raise at that point in more than 50% in comparison with the non-perfused phantoms’ behavior. As furthest spatial points are considered the effect of blood vessel is less noticeable. Thus the use of TU in vascularized tissues requires longer therapeutic sessions or higher TU intensities according to the location of the treatment area. Non-invasive temperature evaluation was performed using computed temporal echo-shifts (TES) proving that, besides the influence of a blood vessel on the overall model performance, TES are still a reliable noninvasive method of monitoring temperature in perfused tissues.

A software tool for intelligent CVA diagnosis by cerebral computerized tomography

The final goal of this work is to create an intelligent support system which assists neuroradiologists to identify Cerebral Vascular Accidents in less time, more precisely. For this purpose, the first step was the creation of a web based tool for registering pathological areas in CT images, which will allow to collect required data for training and testing our proposed classifier, a Radial Basis Function (RBF) based Neural Network.

SINGLE BLACK-BOX MODELS FOR TWO-POINT NON-INVASIVE TEMPERATURE PREDICTION

Abstract 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 °C, as desired for a therapeutic temperature estimator.

Tissue temperature estimation with pulse-echo in blood flow presence

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 transducers 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.

Estimating Temperature in Perfused Tissue Phantoms Subject to Ultrasound Heating

Abstract Thermal therapies using induced ultrasound temperature require more research on spatial-temporal temperature propagation over tissues to allow non-invasive and more efficient treatment. This article presents a comparison between the temperature variations induced by therapeutic ultrasound (TU) on phantoms including or not a blood vessel and reports the usage of a square fitting curve to estimate the temperature based on the temporal echo shifts (TES) computed from the RF-lines signals collected by an imaging ultrasound (IU). Three in-vitro experiments were developed based on a gel phantom: a homogeneous phantom and two perfused phantoms, one mimicking right hepatic artery the other common carotid artery. Phantoms were heated with a TU device emitting continuously and temperature was measured by thermocouples placed 50 mm distant from TU transducer face. Experimental results show that a 3 mm blood vessel could reduce temperature in more than 50 % if placed at the TUs axial line when compared with non-perfused phantom. Despite all the dependencies, results also show that temperature estimation can be accomplished when a second order polynomial is applied through TES results. This study confirms that TES relates nonlinearly with temperature and constitutes a reliable noninvasive method of monitoring temperature in perfused tissues.

Enhancing time-frequency parameters estimation for Doppler Ultrasound blood-flow signals

Doppler Ultrasound (DU) blood flow signals, particularly when collected under intra-operative conditions are noisy; accurate extraction of clinical parameters from their spectra becomes a difficult task. The spectral center frequency and bandwidth were estimated using two estimators with alternative time-frequency resolutions: a fixed resolution method, the Short-Time Fourier Transform (STFT) and the multi-resolution Continuous Wavelet Transform (CWT). Their performance was also assessed when the DU signals were pre-processed by a recently proposed Noise Cancellation Technique (NCTech). The NCTech algorithm enables quantification of the magnitude of the canceled noise in the form of percentage, called Cancellation Level (CL). Quantitative comparisons have been performed in terms of bias of the estimators when four signal-to-noise (SNRs) on DU simulated signals are employed: infinity, 20 dB, 10 dB and 5 dB. Results prove that CWT produced spectral parameters estimates with less bias than STFT; however these estimates were less consistent than the STFT ones. When NCTech is primarily applied to the signal, the STFT is the method to benefit most from this pre-processing technique. The CWT combined with NCTech produced estimates of both spectral parameters with better accuracy over the majority of the cardiac cycle, except where the frequency varies within a small range of frequencies during a short period of time.