Ultrasonic Backscattered Echoes Research Articles

Optimization and Parameters Estimation in Ultrasonic Echo Problems Using Modified Artificial Bee Colony Algorithm

The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape, size, and orientation of the reflectors as well as the microstructure of the propagation path. Accurate estimation of the ultrasonic echo pattern is essential in determining the object or propagation path properties. This paper proposes a parameter estimation method for ultrasonic echoes based on Artificial Bee Colony (ABC) algorithm which is one of the most recent swarm intelligence based algorithms. A modified ABC (MABC) algorithm is given by adding an adjusting factor to the neighborhood search formula of traditional ABC algorithm in order to enhance its performance. The algorithm could overcome the impact of different search range on estimation accuracy to solve the multi-dimensional parameter optimization problems. The performance of the MABC algorithm is demonstrated by numerical simulation and ultrasonic detection experiments. Results show that MABC not only can accurately estimate various parameters of the ultrasonic echoes, but also can achieve the optimal solution in the global scope. The proposed algorithm also has the advantages of fast convergence speed, short running time and real-time parameters estimation.

A statistical model for the ultrasonic backscattered echo from tissue containing microcalcifications

Microcalcifications, generally seen in B-mode breast images as bright spots, often point to the presence of malignancies. Currently, statistical models to describe the signal strengths from tissue containing microcalcifications rely on the Rician or Nakagami densities. Because the Nakagami density requires the existence of randomly located scatterers with random cross sections, microcalcifications manifesting as isolated strong scatterers in the scattering volume makes it less suitable. Additionally, the absence of any regularly spaced scatterers also makes the Rician density less suitable. In this work, it is proposed that a 3-parameter McKay density with heavier tails than the Nakagami density, and hence more severe intensity fluctuations (speckle), might be a better fit to describe echo from tissue containing microcalcifications. This density is developed using a physical description of scattering and its characteristics are explored through random number simulation. Results demonstrate that the McKay densities have higher levels of speckle quantified through the speckle factor than the corresponding gamma densities (Nakagami for the envelope). Some preliminary analysis of tissue-mimicking phantoms modified to include microcalcification-like regions shows that higher speckle factors (seen with the McKay density) can be used to isolate and display microcalcifications. It is expected that this novel approach based on the McKay density will lead to newer and simpler means to detect and identify microcalcifications in ultrasonic B-scans.

Formula omitted] regularized minimum entropy deconvolution for ultrasonic NDT & E

Ultrasonic backscattered echoes can be modeled as a convolution process. A broadband pulse is modulated by a band-limited system, which distort the measured signals. Consequently, deconvolution techniques have been widely used to improve resolution and quality of ultrasonic signals. Without the prior assumption of distortion function, high-order statistics methods are wildly used; one of the optimality criteria is MED (minimum entropy deconvolution). This paper describes an improved approach, which uses the ℓ0−norm regularization algorithm with the minimum entropy based deconvolution, which gives the benefits of faster convergence of algorithms and increase robustness to additive noise and inverse filter length. The ℓ0−norm regularization create a sparsity representation in transform domain, which sparser than time domain can acquire high resolution compared to conventional MED. The kurtosis is used as iterative stop criterion to indicate the minimization of entropy. Moreover, ℓ0−norm regularization can also eliminate the draw back of slow iteration caused by long filter. Simulated results show the efficiency of the algorithms improved in this paper and prove the parameters relationship proposed. Finally the experimental verification is performed on two different specimens with good result obtained. The result show that the methods designed in this paper is more suitable in ultrasonic NDE.

Gaseous Microemboli Sizing in Extracorporeal Circuits Using Ultrasound Backscatter

This paper describes efforts to estimate the size of gaseous microemboli (GME) in extracorporeal blood circuits based on the amplitude of backscattered ultrasound, starting with analytic modeling of the scattering behavior of GME in blood. After neglecting resonance effects, this model predicts a linear relationship between the amplitude of backscattered echoes and the diameter of GME. Computer simulations based on the cylindrical acoustic finite integration technique were performed to test some of the simplifying assumptions of the analytical model, with the simulations predicting small deviations from the linear approximation that could be treated as random scatter. Ultrasonic and microscopic measurements of injected GME were then performed on a test circuit to determine the linear correlation coefficient between echo amplitude and GME diameter in conditions like those employed in real cardiopulmonary bypass (CPB) circuits. The correlation coefficient determined through this study was further validated in a closed-loop CPB circuit using canine blood. This study shows that the amplitude of ultrasonic backscattered echoes can be used to accurately estimate the size distribution of a population of detected GME when the spacing of emboli is great enough to minimize interference and other multi-path scattering effects. With the high flow rates found in CPB circuits, typically ranging from 2 to 6 L per minute (equivalent to a flow velocity of 0.3 to 1 m/s through the circuit tubing), this assumption will be valid even when hundreds of emboli per second pass through the circuit. Therefore, sizing of GME using the ultrasonic backscatter models described in this paper is a viable method for estimating embolic load delivered to a patient during a CPB procedure. (E-mail: lyncht@lunainnovations.com)

The use of the compound probability density function in ultrasonic tissue characterization

Recently, a compound probability density function (pdf) was proposed to model the envelope of the ultrasonic backscattered echo from tissues. This pdf will allow local and global variations in scattering cross sections and even multiple scattering in tissue. It approximates to the Nakagami, K or Rayleigh distributions under different limiting conditions, thus making it very versatile. In this work, a new parameter associated with compound pdf, speckle factor, has been introduced to characterize the scattering conditions. The usefulness of this parameter for tissue characterization has been explored through computer simulation of ultrasonic A scans and analyses of the data collected from tissue-mimicking phantoms. Results suggest the potential applications of the compound pdf and its parameters in ultrasonic tissue characterization.

Model-based estimation of ultrasonic echoes. Part I: analysis and algorithms.

The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape, size, and orientation of the reflectors as well as the microstructure of the propagation path. Accurate estimation of the ultrasonic echo pattern is essential in determining the object/propagation path properties. In this study, we model ultrasonic backscattered echoes in terms of superimposed Gaussian echoes corrupted by noise. Each Gaussian echo in the model is a nonlinear function of a set of parameters: echo bandwidth, arrival time, center frequency, amplitude, and phase. These parameters are sensitive to the echo shape and can be linked to the physical properties of reflectors and frequency characteristics of the propagation path. We address the estimation of these parameters using the maximum likelihood estimation (MLE) principle, assuming that all of the parameters describing the shape of the echo are unknown but deterministic. In cases for which noise is characterized as white Gaussian, the MLE problem simplifies to a least squares (LS) estimation problem. The iterative LS optimization algorithms when applied to superimposed echoes suffer from the problem of convergence and exponential growth in computation as the number of echoes increases. In this investigation, we have developed expectation maximization (EM)-based algorithms to estimate ultrasonic signals in terms of Gaussian echoes. The EM algorithms translate the complicated superimposed echoes estimation into isolated echo estimations, providing computational versatility. The algorithm outperforms the LS methods in terms of independence to the initial guess and convergence to the optimal solution, and it resolves closely spaced overlapping echoes.

Non-Rayleigh statistics of ultrasonic backscattered echo from tissues

The envelope of the backscattered signal from tissues can exhibit non-Rayleigh statistics if the number density of scatterers is small or if the variations in the scattering cross sections are random. The K distribution which has been used extensively in radar, is introduced to model this non-Rayleigh behavior. The generalized K distribution is extremely useful since it encompasses a wide range of distributions such as Rayleigh, Lognormal, and Rician. Computer simulations were conducted using a simple one-dimensional discrete scattering model to investigate the properties of the echo envelope. In addition to cases of low number densities, significant departures from Rayleigh statistics were seen as the scattering cross sections of the scatterers become random. The validity of this model was also tested using data from tissue mimicking phantoms. Results indicate that the density function of the envelope can be modeled by the K distribution and the parameters of the K distribution can provide information on the nature of the scattering region in terms of the number density of the scatterers as well as the scattering cross sections of the scatterers in the range cell. [Work was supported by NSF Grant No. BCS-9207385.]

Signal processing of ultrasonic backscattered echoes for evaluating the microstructure of materials—a review: 42010 Saniie, J.; Bilgutay, N.M.; Wang, T. Signal Processing and Pattern Recognition in Nondestructive Evaluation of Materials, Proceedings of the NATO Advanced Research workshop, Lac Beauport, Quebec, Canada, 19–22 aug. 1987. Edited by C.M. Chen, pp. 87–100. Springer-Verlag (1988)

Signal processing of ultrasonic backscattered echoes for evaluating the microstructure of materials—a review: 42010 Saniie, J.; Bilgutay, N.M.; Wang, T. Signal Processing and Pattern Recognition in Nondestructive Evaluation of Materials, Proceedings of the NATO Advanced Research workshop, Lac Beauport, Quebec, Canada, 19–22 aug. 1987. Edited by C.M. Chen, pp. 87–100. Springer-Verlag (1988)

Performance evaluation of frequency diverse Bayesian ultrasonic flaw detection

The performance of an ultrasonic flaw detection system is valued by the success of differentiating the flaw echoes from those echoes scattered by microstructures (e.g., grains in polycrystalline metals). Microstructure noise (clutter) is stationary and will only vary when the frequency of the ultrasonic beam is changed. In this paper, frequency diverse microstructure information is obtained through the split‐spectrum processing of broadband ultrasonic backscattered echoes. The statistical difference between clutter and flaw echoes over different frequency bands has led to the development of a Bayes classifier that is quadratic and can incorporate the correlation properties of scattered echoes. The performance of the Bayes classifier has been examined for both experimental and computer simulated data, and compared to other commonly used techniques such as mean, minimum, median, and polarity detectors. The Bayes classifier shows superior performance, and the flaw‐to‐clutter visibility has been improved in the range of 5–15 dB when the measured flaw‐to‐clutter ratio is 0 dB or less. These results confirm the feasibility of grain noise suppression by frequency diversity with a subsequent statistical processor for flaw detection.

Spectral analysis of randomly distributed scatterers for ultrasonic grain size estimation

Since the average grain size is an important parameter in material characterization, the non-destructive evaluation of the microstructure in polycrystalline materials using ultrasonic backscattered echoes has significant practical implications. However, the complexity of the ultrasonic backscattered signal, which consists of interrering multiple echoes with random positions and amplitudes, makes accurate grain size evaluation difficult with conventional signal processing techniques. This Paper introduces an alternative approach which examines the spectrum of an ensemble of randomly distributed scatterers with known distribution to establish a relationship between the spectral and temporal statistical properties of the scatterers. Theoretical derivations based on a one-dimensional point scattered model show that the average grain size can be extracted from the power spectrum of the backscattered echoes. Using a non-linear least-squares technique, the theoretical grain power spectrum is curve-fitted to the sample grain power spectrum to estimate the mean and standard deviation of the grain size. Computer simulations based on this technique show that the average grain size can be extracted from the power spectrum of the backscattered echoes, indicating the potential feasibility of this technique for material characterization.