Post-compression Signals Research Articles

Convolutional Neural Network-Based Speckle Tracking for Ultrasound Strain Elastography: An Unsupervised Learning Approach

Accurate and computationally efficient motion estimation is a critical component of real-time ultrasound strain elastography (USE). With the advent of deep-learning neural network models, a growing body of work has explored supervised convolutional neural network (CNN)-based optical flow in the framework of USE. However, the above-said supervised learning was often done using simulated ultrasound data. The research community has questioned whether simulated ultrasound data containing simple motion can train deep-learning CNN models that can reliably track complex in vivo speckle motion. In parallel with other research groups' efforts, this study developed an unsupervised motion estimation neural network (UMEN-Net) for USE by adapting a well-established CNN model named PWC-Net. Our network's input is a pair of predeformation and postdeformation radio frequency (RF) echo signals. The proposed network outputs both axial and lateral displacement fields. The loss function consists of a correlation between the predeformation signal and the motion-compensated postcompression signal, smoothness of the displacement fields, and tissue incompressibility. Notably, an innovative correlation method known as the globally optimized correspondence (GOCor) volumes module developed by Truong et al. was used to replace the original Corr module to enhance our evaluation of signal correlation. The proposed CNN model was tested using simulated, phantom, and in vivo ultrasound data containing biologically confirmed breast lesions. Its performance was compared against other state-of-the-art methods, including two deep-learning-based tracking methods (MPWC-Net++ and ReUSENet) and two conventional tracking methods (GLUE and BRGMT-LPF). In summary, compared with the four known methods mentioned above, our unsupervised CNN model not only obtained higher signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) for axial strain estimates but also improved the quality of the lateral strain estimates.

A 2D strain estimator with numerical optimization method for soft-tissue elastography

Elastography is a bioelasticity-based imaging modality which has been proved to be a potential evaluation tool to detect the tissue abnormalities. Conventional method for elastography is to estimate the displacement based on cross-correlation technique firstly, then strain profile is calculated as the gradient of the displacement. The main problem of this method arises from the fact that the cross-correlation between pre- and post-compression signals will be decreased because of the signal’s compression-to-deformation. It may constrain the estimation of the displacement. Numerical optimization, as an efficient tool to estimate the non-rigid deformation in image registration, has its potential to achieve the elastogram. This paper incorporates the idea of image registration into elastography and proposes a radio frenquency (RF) signal registration strain estimator based on the minimization of a cost function using numerical optimization method with Powell algorithm (NOMPA). To evaluate the proposed scheme, the simulation data with a hard inclusion embedded in the homogeneous background is produced for analysis. NOMPA can obtain the displacement profiles and strain profiles simultaneously. When compared with the cross-correlation based method, NOMPA presents better signal-to-noise ratio (SNR, 32.6 ± 1.5 dB vs. 23.8 ± 1.1 dB) and contrast-to-noise ratio (CNR, 28.8 ± 1.8 dB vs. 21.7 ± 0.9 dB) in axial normal strain estimation. The in vitro experiment of porcine liver with ethanol-induced lesion is also studied. The statistic results of SNR and CNR indicate that strain profiles by NOMPA performs better anti-noise and target detectability than that by cross-correlation based method. Though NOMPA carry a heavier computational burden than cross-correlation based method, it may be an useful method to obtain 2D strains in elastography.

Radio‐frequency ablation electrode displacement elastography: A phantom study

This article describes the evaluation of a novel method of tissue displacement for use in the elastographic visualization of radio-frequency (rf) ablation-induced lesions. The method involves use of the radio-frequency ablation electrode as a displacement device, which provides localized compression in the region of interest. This displacement mechanism offers the advantage of easy in vivo implementation since problems such as excessive lateral and elevational displacements present when using external compression are reduced with this approach. The method was tested on a single-inclusion tissue-mimicking phantom containing a radio-frequency ablation electrode rigidly attached to the inclusion center. Full-frame rf echo signals were acquired from the phantom before and after electrode displacements ranging from 0.05 to 0.2 mm. One-dimensional cross-correlation analysis between pre- and postcompression signals was used to measure tissue displacements, and strains were determined by computing the gradient of the displacement. The strain contrast, contrast-to-noise ratio, and signal-to-noise ratio were estimated from the resulting strain images. Comparisons are drawn between the elastographically measured dimensions and those known a priori for the single-inclusion phantom. Electrode displacement elastography was found to slightly underestimate the inclusion dimensions. The method was also tested on a second tissue-mimicking phantom and on in vitro rf-ablated lesions in canine liver tissue. The results validate previous in vivo findings that electrode displacement elastography is an effective method for monitoring rf ablation.

Tissue strain imaging using a wavelet transform-based peak search algorithm

A new method is proposed to estimate the motion and relative local compression between two successive ultrasound RF signals under different compression states. The algorithm uses the continuous wavelet transform to locate the peaks in the RF signals. The estimated peaks in the pre- and post-compression signals are assigned to each other by a peak matching technique with the goal of minimizing the number of false matches. The method allows local shifts of the tissue to be estimated. The method has been tested in one-dimensional simulations and phantom experiments. The signal-to-noise ratio and the rms error are shown to be better than for the standard cross-correlation method (CC). The new estimator remains unbiased for up to 10% strain which is a larger range than that of CC. The maximum signal-to-noise ratio is 3 times as high as that of the CC method, showing higher sensitivity as well. The method is computationally efficient, achieving 0.7 msec/RF line on a standard personal computer.

A novel and robust method for rapid strain estimation in elastography.

In elastography, change in signal shape from tissue deformation and nonaxial tissue motion reduce correlation between the pre- and postcompression echo signals. Appropriate global temporal stretching of postcompression signals can reduce the decorrelation. Adaptive stretching performs a search for the stretch factor that maximizes the correlation between the pre- and postcompression echo signal segments at each data window location. Adaptive stretching is robust but computation intensive. In contrast, global stretching is fast but performs well only in areas where local strains are close to the applied strain. We developed a method that strikes a balance between the speed of global stretching and the performance of adaptive stretching. In this method, several strain maps are computed by performing global stretching with a range of different stretch factors. The area in each computed strain image with strain values closely corresponding to the uniform stretch factor will contain 'good quality' strain estimates. To produce a single elastogram at the end, we identify the strain map with the maximum correlation at each location and the strain value in that strain map at that location is chosen for the combined map. Results from data generated by finite-element simulation and phantom experiments demonstrate that the described strain estimator is significantly less susceptible to signal degradation than conventional strain estimators.

Analysis of an adaptive strain estimation technique in elastography.

Elastography is based on the estimation of strain due to tissue compression or expansion. Conventional elastography involves computing strain as the gradient of the displacement (time-delay) estimates between gated pre- and postcompression signals. Uniform temporal stretching of the postcompression signals has been used to reduce the echo-signal decorrelation noise. However, a uniform stretch of the entire postcompression signal is not optimal in the presence of strain contrast in the tissue and could result in loss of contrast in the elastogram. This has prompted the use of local adaptive stretching techniques. Several adaptive strain estimation techniques using wavelets, local stretching and iterative strain estimation have been proposed. Yet, a quantitative analysis of the improvement in quality of the strain estimates overconventional strain estimation techniques has not been reported. We propose a two-stage adaptive strain estimation technique and perform a quantitative comparison with the conventional strain estimation techniques in elastography. In this technique, initial displacement and strain estimates using global stretching are computed, filtered and then used to locally shift and stretch the postcompression signal. This is followed by a correlation of the shifted and stretched postcompression signal with the precompression signal to estimate the local displacements and hence the local strains. As proof of principle, this adaptive stretching technique was tested using simulated and experimental data.

Phase aberration effects in elastography

In sonography, phase aberration plays a role in the corruption of sonograms. Phase aberration does not have a significant impact on elastography, if statistically similar phase errors are present in both the pre- and postcompression signals. However, if the phase errors are present in only one of the pre- or postcompression signal pairs, the precision of the strain estimation process will be reduced. In some cases, increased phase errors may occur only in the postcompression signal due to changes in the tissue structure with the applied compression. Phase-aberration effects increase with applied strain and may be viewed as an image quality derating factor, much like frequency-dependent attenuation or undesired lateral tissue motion. In this paper, we present a theoretical and simulation study of the effects of phase aberration on the elastographic strain-estimation process, using the strain filter approach. (E-mail: tvarghese@facstaff.wisc.edu)

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.

A deconvolution filter for improvement of time-delay estimation in elastography

In elastography, tissue under investigation is compressed, and the resulting strain is estimated from the gradient of displacement estimates. Therefore, it is important to accurately estimate the displacements (time-delay) for good quality elastograms. A principal source of error in time-delay estimation in elastography is the decorrelation of the echo signal due to tissue compression (decorrelation noise). Temporal stretching of the postcompression signals has been shown to reduce the decorrelation noise at small strains. In this article, we present a deconvolution filter that reduces the decorrelation even further when applied in conjunction with signal stretching. The performance of the proposed filter is evaluated using simulated data.

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.

Reduction of signal decorrelation from mechanical compression of tissues by temporal stretching: Applications to elastography

Elastography is based on the estimation of strain due to tissue compression. Strain is computed from the estimates of time delays between gated precompression and postcompression echo signals. Time delay estimates are obtained from the location of the peak of the crosscorrelation function between gated precompression and postcompression signals. It is of paramount importance to accurately estimate the time delays for good quality elastograms. A main source of time delay estimation (TDE) error in elasticity imaging is the decorrelation of the echo signal as a result of tissue compression (decorrelation noise). The effect of decorrelation on the mean of the cross-correlation function and the correlation coefficient has been investigated. The expected value of the cross-correlation function between the precompression and postcompression signals was shown to be a filtered version of the autocorrelation function of the precompression signal. In this article, the effect of temporal stretching of the postcompression echo signal on the crosscorrelation function will be investigated along the same line. The applied compression is assumed to be uniform; the decorrelations introduced by the lateral and elevational tissue movements are ignored. The theory predicts that if the postcompression echo signals are stretched before the TDE step, then for small strains, the cross-correlation function very closely resembles the autocorrelation function. For larger strains, correlation is improved if temporal stretching is applied. The theory is corroborated by results from simulation and homogeneous phantom experiments. Thus, the decorrelation noise in elastograms can be reduced by temporal stretching of the postcompression signal.

Noise reduction in elastograms using temporal stretching with multicompression averaging

Elastography uses estimates of the time delay (obtained by cross-correlation) to compute strain estimates in tissue due to quasistatic compression. Because the time delay estimates do not generally occur at the sampling intervals, the location of the cross-correlation peak does not give and accurate estimate of the time delay. Sampling errors in the time-delay estimate are reduced using signal interpolation techniques to obtain subsample time-delay estimates. Distortions of the echo signals due to tissue compression introduce correlation artifacts in the elastogram. These artifacts are reduced by a combination of small compressions and temporal stretching of the postcompression signal. Random noise effects in the resulting elastograms are reduced by averaging several elastograms, obtained from successive small compressions (assuming that the errors are uncorrelated). Multicompression averaging with temporal stretching is shown to increase the signal-to-noise ratio in the elastogram by an order of magnitude, without sacrificing sensitivity, resolution or dynamic range. The strain filter concept is extended in this article to theoretically characterize the performance of multicompression averaging with temporal stretching.

Reduction of Image Noise in Elastography

Elastography is a method for imaging the elastic properties of compliant tissues which produces gray scale elasticity images called elastograms. The elastograms of phantoms with homogeneous elastic properties exhibit a noisy appearance. We demonstrate that this noisy appearance of the elastograms is due to the nonstationary relationship between the pre- and postcompression signals that results in an artifactual modulation of the strain estimates by the amplitude variations of the envelope of the rf signal. We have identified two methods to reduce the strain modulation artifact. The first method consists of reducing the signal amplitude swings within the observation windows by logarithmically or otherwise compressing the rf signal. The sensitivity of this method to amplitude compression strength and the ability to reduce the noise in the elastograms without affecting the spatial resolution are investigated through simulations. The second method to reduce the strain modulation artifact consists of temporal stretching of the signal obtained after physical compression to approximate the shape of the signal obtained before compression. In this paper, we discuss the first method. The results show that significant improvement in image noise can be obtained with logarithmic amplitude compression. This improvement is obtained in conjunction with improved spatial resolution.

Reduction of Image Noise in Elastography

Elastography is a method for imaging the elastic properties of compliant tissues which produces gray scale elasticity images called elastograms. The elastograms of phantoms with homogeneous elastic properties exhibit a noisy appearance. We demonstrate that this noisy appearance of the elastograms is due to the nonstationary relationship between the pre- and postcompression signals that results in an artifactual modulation of the strain estimates by the amplitude variations of the envelope of the rf signal. We have identified two methods to reduce the strain modulation artifact. The first method consists of reducing the signal amplitude swings within the observation windows by logarithmically or otherwise compressing the rf signal. The sensitivity of this method to amplitude compression strength and the ability to reduce the noise in the elastograms without affecting the spatial resolution are investigated through simulations. The second method to reduce the strain modulation artifact consists of temporal stretching of the signal obtained after physical compression to approximate the shape of the signal obtained before compression. In this paper, we discuss the first method. The results show that significant improvement in image noise can be obtained with logarithmic amplitude compression. This improvement is obtained in conjunction with improved spatial resolution.