Ultrasonic data compression via parameter estimation

Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.     

Wavelet transform-based strain estimator for elastography

A new signal processing algorithm based on a wavelet transform (WT) is proposed for instantaneous strain estimation in acoustic elastography. The proposed estimator locally weighs ultrasonic echo signals acquired before tissue compression by a Gaussian window function and uses the resulting waveform as a mother wavelet to calculate the WT of the postcompression signal. From the location of the WT peak, strain is estimated in the time-frequency domain. Because of the additive noise in signals and the discrete sampling, errors are commonly made in estimating the strain. Statistics of these errors are analyzed theoretically to evaluate the performance of the proposed estimator. The strain estimates are found to be unbiased, but error variances depend on the signal properties (echo signal-to-noise ratio and bandwidth), signal processing parameter (time-bandwidth product), and the applied strain. The results are compared with those obtained from the conventional strain estimator based on time-delay estimates. The proposed estimator is shown to offer strain estimates with greater precision and potentially higher spatial resolution, dynamic range, and sensitivity at the expense of increased computation time.     

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     

基于EEMD和低秩稀疏分解的超声缺陷回波检测方法

针对应用超声对金属材料微小缺陷检测时缺陷回波容易被噪声干扰的问题,提出了一种基于集合经验模态分解(EEMD)和低秩稀疏分解相结合的检测方法,以避免传统基于经验模态分解(EMD)的去噪方法难以消除结构噪声的问题.首先对缺陷检测信号进行EEMD得到一系列本征模态函数(IMF),采用基于概率密度函数的相似性测量方法选取相关模...     

一种新的估计多项式相位信号瞬时频率的参数化时频分析方法

通过多项式非线性核函数取代线性调频小波变换中的线性核函数,提出一种新的参数化时频分析方法：非线性调频小波变换。对瞬时频率是时间任意连续函数的信号而言,选择合适的多项式核特征参数,非线性调频小波变换的时频分布有良好的时频聚集性。应用非线性调频小波变换分析任意阶次多项式相位信号。由于非线性调频小波变换的性能取决于多项式核特征参数,本文还给出非线性调频小波变换的核特征参数估计算法,进一步可实现多项式相位信号的瞬时频率和参量估计。仿真信号验证算法的有效性。     

EMAT noise suppression using information fusion in stationary wavelet packets

An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering, and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for electromagnetic acoustic transducer (EMAT) pulse-echo signals, and therefore do not exploit their unique impulse nature. The approach to ultrasonic signal filtering proposed in this paper is based on stationary wavelet packet denoising with a threshold influenced by several information sources: a statistical echo detection, the amplitude distribution of the wavelet transform coefficients, and a priori known system frequency characteristics. The proposed method was evaluated on signals measured with EMAT probes and under various SNR conditions; it outperforms the wavelet transform with the Stein unbiased risk estimate (SURE) threshold estimation method and split-spectrum processing (SSP). The results indicate SNR enhancement of 19 dB with real EMAT data.     

基于提升小波变换的超声信号消噪技术

为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR）,提出了一种基于提升小波变换多分辨率分析的超声信号消噪新技术.在分析传统裂谱分析（SSP）方法原理及其局限性的基础上,通过采用提升小波变换多分辨率分析能力将原始超声回波信号进行子带分解,然后按照一定的信噪分离规则来消除噪声,达到提高信噪比的目的.实验结果表明,与传统裂谱分析方法相比,该方法增强了消噪性能的稳定性,提高了超声回波信号的信噪比.     

基于小波变换的裂谱分析法

 为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR）,提出了一种基于小波变换多分辨率分析的裂谱分析新方法．该方法在分析传统裂谱分析（SSP）方法原理及其局限性的基础上,通过采用小波变换多分辨率分析能力将原始超声回波信号进行等Q子带分解,然后按照一定的信噪分离规则来消除噪声,达到提高信噪比的目的．实验结果表明,与传统裂谱分析方法相比,该方法提高了消噪性能的稳定性,增强了湮没晶粒(或其他散射体)散射中的缺陷回波信号能力．     

基于经验模态分解的管道超声回波信号噪声消除

在管道超声无损检测中,超声回波信号往往受到电子噪声、结构噪声等噪声的影响,所以在分析缺陷回波信号时,必须对回波信号进行去噪处理.本文提出了一种新型的基于经验模态分解的方法对超声回波信号进行了良好的消噪处理.通过计算,超声回波信号的信噪比大约提高了11 dB.     

Quantitative ultrasonic characterization of diffuse scatterers in the presence of structures that produce coherent echoes

Quantitative ultrasound (QUS) techniques that parameterize the backscattered power spectrum have demonstrated significant promise for ultrasonic tissue characterization. Some QUS parameters, such as the effective scatterer diameter (ESD), require the assumption that the examined medium contains uniform diffuse scatterers. Structures that invalidate this assumption can significantly affect the estimated QUS parameters and decrease performance when classifying disease. In this work, a method was developed to reduce the effects of echoes that invalidate the assumption of diffuse scattering. To accomplish this task, backscattered signal sections containing non-diffuse echoes were identified and removed from the QUS analysis. Parameters estimated from the generalized spectrum (GS) and the Rayleigh SNR parameter were compared for detecting data blocks with non-diffuse echoes. Simulations and experiments were used to evaluate the effectiveness of the method. Experiments consisted of estimating QUS parameters from spontaneous fibroadenomas in rats and from beef liver samples. Results indicated that the method was able to significantly reduce or eliminate the effects of nondiffuse echoes that might exist in the backscattered signal. For example, the average reduction in the relative standard deviation of ESD estimates from simulation, rat fibroadenomas, and beef liver samples were 13%, 30%, and 51%, respectively. The Rayleigh SNR parameter performed best at detecting nondiffuse echoes for the purpose of removing and reducing ESD bias and variance. The method provides a means to improve the diagnostic capabilities of QUS techniques by allowing separate analysis of diffuse and non-diffuse scatterers.     

An adaptive strain estimator for elastography

Elastography is based on the estimation of strain due to applied tissue compression. In conventional elastography, strain is computed from the gradient of the displacement estimates between gated pre- and postcompression echo signals. Gradient-based estimation methods are known to be susceptible to noise. In elastography, in addition to the electronic noise, a principal source of estimation error is the decorrelation of the echo signal as a result of tissue compression (decorrelation noise). Temporal stretching of postcompression signals previously was shown to reduce the decorrelation noise. In this paper, we introduce a novel estimator that uses the stretch factor itself as an estimator of the strain. It uses an iterative algorithm that adaptively maximises the correlation between the pre- and postcompression echo signals by appropriately stretching the latter. We investigate the performance of this adaptive strain estimator using simulated and experimental data. The estimator has exhibited a vastly superior performance compared with the conventional gradient-based estimator.     

Complex principal components for robust motion estimation

Bias and variance errors in motion estimation result from electronic noise, decorrelation, aliasing, and inherent algorithm limitations. Unlike most error sources, decorrelation is coherent over time and has the same power spectrum as the signal. Thus, reducing decorrelation is impossible through frequency domain filtering or simple averaging and must be achieved through other methods. In this paper, we present a novel motion estimator, termed the principal component displacement estimator (PCDE), which takes advantage of the signal separation capabilities of principal component analysis (PCA) to reject decorrelation and noise. Furthermore, PCDE only requires the computation of a single principal component, enabling computational speed that is on the same order of magnitude or faster than the commonly used Loupas algorithm. Unlike prior PCA strategies, PCDE uses complex data to generate motion estimates using only a single principal component. The use of complex echo data is critical because it allows for separation of signal components based on motion, which is revealed through phase changes of the complex principal components. PCDE operates on the assumption that the signal component of interest is also the most energetic component in an ensemble of echo data. This assumption holds in most clinical ultrasound environments. However, in environments where electronic noise SNR is less than 0 dB or in blood flow data for which the wall signal dominates the signal from blood flow, the calculation of more than one PC is required to obtain the signal of interest. We simulated synthetic ultrasound data to assess the performance of PCDE over a wide range of imaging conditions and in the presence of decorrelation and additive noise. Under typical ultrasonic elasticity imaging conditions (0.98 signal correlation, 25 dB SNR, 1 sample shift), PCDE decreased estimation bias by more than 10% and standard deviation by more than 30% compared with the Loupas method and normalized cross-correlation with cosine fitting (NC CF). More modest gains were observed relative to spline-based time delay estimation (sTDE). PCDE was also tested on experimental elastography data. Compressions of approximately 1.5% were applied to a CIRS elastography phantom with embedded 10.4-mm-diameter lesions that had moduli contrasts of -9.2, -5.9, and 12.0 dB. The standard deviation of displacement estimates was reduced by at least 67% in homogeneous regions at 35 to 40 mm in depth with respect to estimates produced by Loupas, NC CF, and sTDE. Greater improvements in CNR and displacement standard deviation were observed at larger depths where speckle decorrelation and other noise sources were more significant.     

Signal matching wavelet for ultrasonic flaw detection in high background noise

The wavelet transform (WT) is widely used in ultrasonic flaw detection (UFD) systems because of its property of multiresolution time-frequency analysis. Those traditional WT-based methods for UFD use a wavelet basis with limited types to match various echo signals (called wavelet matching signals), so it is difficult for those methods to achieve the optimal match between echo signal and wavelet basis. This results in limited detection ability in high background noise for those WT-based methods. In this paper, we propose a signal matching wavelet (SMW) method for UFD to solve this problem. Unlike traditional UFD systems, in the proposed SMW, the transmitted signal is designed to be a wavelet function for matching a wavelet basis. This makes it possible to obtain the optimal match between the echo signal and the wavelet basis. To achieve the optimal match from the aspect of energy, we derive three rules for designing the transmitted signal and selecting the wavelet basis. Further, the parameter selection in applying the proposed SMW method to a practical UFD system is analyzed. In addition, a low-rate discrete WT structure is designed to decrease the hardware cost, which facilitates the practical application of the proposed SMW. The simulation results show that the proposed SMW can efficiently detect flaws in high background noise even with SNR lower than -20 dB, outperforming the existing methods by 5 dB.     

调频初相位补偿的广义Warblet变换时频分析

信号时频分析的长时间窗时频分析法通常可提高输出信噪比和频率分辨率,但对于调频信号,会降低线谱时频能量聚集度并影响瞬时频率估计。对于调频信号广义Warblet变换(Generalized Warblet Transform,GWT),具有较短时傅里叶变换(Short Time Fourier Transform,STFT)更优的时频分析性能,但在长时间窗分析时,调频初相位估计误差会使算法性能下降甚至失效。针对该问题,提出调频初相位补偿的GWT(Frequency Modulation Initial Phase CompensationGWT,FMIPC-GWT)时频分析方法。在调频参数估计时将一半时间窗长所经过的相位补偿到调频初相位中,提高调频参数估计的准确性以增加瞬时频率估计精度。仿真和实验数据验证了,相比STFT法和GWT法,FMIPC-GWT法对于非线性调频信号时频分析性能更优。FMIPC-GWT法在调频信号线谱检测与瞬时频率估计等方面具有应用前景。     

A measurement method based on Kalman filtering for ultrasonic time-of-flight estimation

A new digital signal-processing method for ultrasonic time-of-flight (TOF) estimation is presented. The method applies the discrete extended Kalman filter (DEKF) to the acquired ultrasonic signal in order to accurately estimate the shape factors of the echo envelope as well as locate its onset. It is also possible to assure reduced bias and uncertainty in critical TOF measurements, such as those involving low signal-to-noise ratio (SNR) as well as severe distortion of echo shape. A number of numerical tests are conducted on simulated signals with the aim of highlighting the good performance of the method when operating in critical conditions. Results attained in TOF-based distance measurements finally assess the reliability and efficacy of the method in the presence of actual ultrasonic signals.     

Estimating ultrasonic time-of-flight through quadrature demodulation

A new digital signal-processing (DSP) method for ultrasonic time-of-flight (TOF) estimation is presented hereinafter. The method applies a traditional quadrature-demodulation scheme to the acquired ultrasonic signal in order to suitably extract the envelope of the main echo and locate its onset (i.e., the starting time of the echo). It is also possible to assure reduced bias and uncertainty in critical TOF measurements, such as those involving low signal-to-noise ratio (SNR) as well as severe distortion of echo shape. A number of numerical tests are conducted on simulated signals with the aim of highlighting the good performance of the method when operating in critical conditions; to reduce the computational burden without losing significance in the analysis, a statistical experimental design-based technique is enlisted. Results attained in TOF-based distance measurements finally assess the method's reliability and efficacy even in the presence of actual ultrasonic signals.     

超声波在水中衰减的频率效应及其对脉冲回波的影响

超声波在传播中会发生幅值衰减,该衰减不仅与超声波传播的距离有关,还与超声波的频率有关。为了研究高频超声波衰减的频率效应,本文通过脉冲回波法分析脉冲超声波在水中传播反射回波的幅值和频谱变化,研究了超声波在水中传播时幅值衰减与传播距离及其与超声频率之间的关系,通过测量脉冲超声波在水中传播不同距离时的反射回波,并对其进行傅里叶变换,分析了超声波传播衰减的距离效应和频率效应。研究发现:超声波在水中的传播衰减随距离呈指数规律,且不同频率超声波的衰减系数不相同,频率越高,衰减越大,衰减的频率效应可有效解释反射法高频脉冲超声检测中回波脉冲信号的中心频率远低于换能器标称中心频率的现象。     

钢包耳轴根部焊缝的相控阵超声检测与信号处理

采用匹配追踪算法和小波变换对低信噪比(2dB和-5dB)钢包耳轴根部焊缝缺陷检测信号进行预处理对比分析,在定制试块上采用64阵元超声相控阵探头对深度为230mm的耳轴根部焊缝进行检测,并且结合基于Gabor原子库的匹配追踪算法对回波信号进行消噪后成像.结果表明,在低信噪比(-5dB)条件下,匹配追踪算法较小波变换有更好的预处理效果;对深度位于220mm和230mm处的钢包耳轴焊缝缺陷的实际检测信号进行去噪处理时,匹配追踪算法能够准确定位缺陷位置并显著提高缺陷处信噪比.     

Model-based estimation of ultrasonic echoes. Part II: Nondestructive evaluation applications

For Part I see ibid., vol.48, no.3, pp.787-802 (2001). Accurate estimation of the ultrasonic echo pattern leading to the physical property of the object is desirable for ultrasonic NDE (nondestructive evaluation) applications. In Part I of this study, we have presented a generalized parametric ultrasonic echo model, composed of a number of Gaussian echoes corrupted by noise, and algorithms for accurately estimating the parameters. In Part II of this study, we explore the merits of this model-based estimation method in ultrasonic applications. This method produces high resolution and accurate estimates for ultrasonic echo parameters, i.e., time of flight (TOF) amplitude, center frequency, bandwidth, and phase. Furthermore, it offers a solution to the deconvolution problem for restoration of the target response, i.e., ultrasonic reflection and transmission properties of materials, from the backscattered echoes. The model-based estimation method makes deconvolution possible in the presence of significant noise. It can also restore closely spaced overlapping echoes beyond the resolution of the measuring system. These properties of the estimation method are investigated in various ultrasonic applications such as transducer pulse-echo wavelet estimation, subsample time delay estimation, and thickness sizing of thin layers     

基于优化匹配追踪稀疏分解的微弱超声回波提取

为提高低信噪比环境中微弱超声回波信号的提取性能,提出优化的匹配追踪(Matching Pursuit,MP)稀疏分解的超声回波提取算法。该算法将具有连续空间搜索能力的粒子群优化(Particle Swarm Optimization,PSO)算法引入到MP稀疏分解中,以缓解原子集的遍历有限性需求与超完备性之间的矛盾,通过改进粒子群算法的参数自适应设置及MP算法的目标函数和重构函数,实现自适应的PSO-MP稀疏分解算法,并建立了连续伽柏(Gabor)原子集,提高了最优原子与不同参数超声回波信号的匹配程度,最后由最优原子集通过重构函数对回波信号进行重构,实现对回波的降噪和准确提取。实验结果表明,该算法显著降低了计算量,效果优于已有小波阈值等算法且具有较好鲁棒性。