Subarray coherence based postfilter for eigenspace based minimum variance beamformer in ultrasound plane-wave imaging

Far-focus compound ultrasound imaging with lag-one coherence-based zero-cross factor

BACKGROUND

Ultrasound imaging has been widely used in clinical examination because of portability, safety, and low cost. However, there are still some main challenges of imaging quality that remain in conventional ultrasound systems.

OBJECTIVE

Improving image quality of SA-based methods using an improved imaging mode named far-focus compound (FSC) imaging.

METHODS

A far-focus compound (FSC) imaging based on full-aperture transmission and full-aperture reception is proposed in this paper. In transmission, it uses the full aperture to transmit the focused beam to ensure image resolution and emission of sound field energy. In reception, the full aperture is used to receive the reflected beam to ensure the image quality. A lag-one coherence-based zero-cross factor (LOCZF) is then implemented in FSC for improvement of contrast ratio (CR). The LOCZF uses lag-one coherence as zero-cross factorâs adaptive coefficient. Comparisons were made with several other weighting techniques by performing simulations and experiments for performance evaluation.

RESULTS

Results confirm that LOCZF applied to FSC offers a good image contrast and simultaneously the speckle pattern. For simulated cysts, CR improvement of LOCZF reaches 194.1%. For experimental cysts, CR improvement of LOCZF reaches 220%. From the in-vivo result, compared with FSC, CR improvement of LOCZF reaches 112.7%.

CONCLUSION

Proved gCNR performance. In addition, the LOCZF method shows good performance in experiments. The proposed method can be used as an effective weighting technique for improvement of image quality in ultrasound imaging.

Adaptive subarray coherence based post-filter using array gain in medical ultrasound imaging

This paper presents an adaptive subarray coherence-based post-filter (ASCBP) applied to the eigenspace-based forward-backward minimum variance (ESB-FBMV) beamformer to simultaneously improve image quality and beamformer robustness. Additionally, the ASCBP can separate close targets. The ASCBP uses an adaptive noise power weight based on the concept of the beamformer's array gain (AG) to suppress the noise adaptively and achieve improved images. Moreover, a square neighborhood average was applied to the ASCBP in order to provide more smoothed square neighborhood ASCBP (SN-ASCBP) values and improve the speckle quality. Through simulations of point phantoms and cyst phantoms and experimental validation, the performance of the proposed methods was compared to that of delay-and-sum (DAS), MV-based beamformers, and subarray coherence-based post-filter (SCBP). The simulated results demonstrated that the ASCBP method improved the full width at half maximum (FWHM) by 57 % and the coherent interference suppression power (CISP) by 52 dB compared to the SCBP post-filter. Considering the experimental results, the SN-ASCBP method presented the best enhancement in terms of generalized contrast to noise ratio (gCNR) and contrast ratio (CR) while the ASCBP showed the best improvement in FWHM among other methods. Furthermore, the proposed methods presented a striking performance in low SNRs. The results of evaluating the different methods under aberration and sound speed error illustrated the better robustness of the proposed methods in comparison with others.

Experimental Study of Aperiodic Plane Wave Imaging for Ultrafast 3-D Ultrasound Imaging

OBJECTIVE

Although plane wave imaging (PWI) with multiple plane waves (PWs) steered at different angles enables ultrafast three-dimensional (3-D) ultrasonic imaging, there is still a challenging tradeoff between image quality and frame rate. To address this challenge, we recently proposed the aperiodic PWI (APWI) with mathematical analysis and simulation study. In this paper, we demonstrate the feasibility of APWI and evaluate the performance with phantom and in vivo experiments.

METHODS

APWI with a concentric ring angle pattern (APWI-C) and APWI with a sunflower pattern (APWI-S) are evaluated. For experimental verification of the methods, the experimental results are compared with simulation results in terms of the mainlobe-to-sidelobe ratio. In addition, the performance of APWI is compared with that of conventional PWI by using a commercial phantom. To examine the potential for clinical use of APWI, a gallstone-mimicking phantom study and an in vivo carotid artery experiment are also conducted.

RESULTS

In the phantom study, the APWI methods provide a contrast ratio approximately 23 dB higher than that of PWI. In a gallstone mimicking experiment, the proposed methods yield 3-D rendered stone images more similar to the real stones than PWI. In the in vivo carotid artery images, APWI reduces the clutter artifacts inside the artery.

CONCLUSION

Phantom and in vivo studies show that the APWI enhances the contrast without compromising the spatial resolution and frame rate.

SIGNIFICANCE

This study experimentally demonstrates the feasibility and advantage of APWI for ultrafast 3-D ultrasonic imaging.

Adaptive scaled coherence factor for ultrasound pixel-based beamforming

Synthetic aperture (SA) ultrasound imaging can obtain images with high-resolution owing to its ability to dynamically focus in both directions. The signal-to-noise ratio (SNR) of SA imaging is poor because the pulse energy using one array element is quite low. Thus, the SA method with bidirectional pixel-based focusing (SA-BiPBF) was previously proposed as a solution to this challenge. However, using the nonadaptive delay-and-sum (DAS) beamforming still limits its imaging performance. This study proposes an adaptive scaled coherence factor (AscCF) for SA-BiPBF to further boost the image quality. The AscCF exploits generalized coherence factor (GCF) to measure the signal coherence to adaptively adapt the parameters in SNR estimation rather than fixed ones. Comparisons were made with several other weighting techniques by performing simulations and experiments for performance evaluation. Results confirm that AscCF applied to SA-BiPBF offers a good image contrast while reservation of the speckle pattern. AscCF achieves maximal improvements of contrast ratio (CR) by 48.5% and 47.76 % compared with scaled coherence factor (scCF), respectively in simulation and experiment. Simultaneously, the maximum of improvements in speckle signal-to-noise ratio (sSNR) of AscCF are 11.28 % and 20.01 % upon scCF in simulation and experiment, respectively. From the in vivo result, it also appears a potential for AscCF to act in clinical situations to better detect lesion and retain speckle pattern.

A submatrix spatial coherence approach to minimum variance beamforming combined with sign coherence factor for coherent plane wave compounding

BACKGROUND:

The coherent plane wave compounding (CPWC) is a promising technique to enhance the imaging quality while maintaining the high frame rate in the plane wave ultrasound imaging. Recently, the spatial-coherence-based method has been specially designed to process echo matrix required by the minimum variance (MV) method.

OBJECTIVE:

In this paper, a novel beamforming method that integrates the submatrix-spatial-coherence-based MV with the sign coherence factor (SCF) is proposed to further improve the imaging quality.

METHOD:

The submatrix smoothing technique is modified to smooth and de-correlate signals of the receiving array dimension. Then, the SCF is used to modify the input vector of the beamformer, which can reduce side lobe noises with almost no increase in the amount of calculation. Simulation, phantom, in vivo, and sound velocity error experiments have been performed to verify the superiority of the proposed beamformer.

RESULTS:

The imaging results show that the proposed approach performs better in the imaging resolution and contrast compared to the traditional CPWC method.

CONCLUSION:

The robustness of the proposed method is enhanced, and the over-suppression phenomenon can be alleviated, which is a phenomenon that occurs in the original spatial-coherence and SCF methods.

Adaptive Spectral Doppler Estimation Based on the Modified Amplitude Spectrum Capon

In conventional ultrasound systems, the compromise between frequency and temporal resolution limits the quality of the spectrograms and the ability to track fast blood flows. The main objective of this study was to identify a method that could reduce spectral broadening over time by reducing the observations and improving the spectral resolution and contrast. This problem is more pronounced in the process of imaging at higher blood velocities when using a short Doppler signal observation window (OW) in adaptive methods. The proposed adaptive technique, which is based on the covariance matrix Eigen space and the amplitude spectrum Capon (ASC) algorithm, managed to improve the spectral resolution and contrast compared with other adaptive algorithms within a shorter observation time, and it offered a narrower power spectrum and a more accurate spectrogram over time in combination with a coherence-based post filter. All methods were tested through various simulations. First, an analysis was carried out by simulating the femoral artery flow and the time-independent parabolic flow using the Field II simulator. Then, the performance of the proposed method was evaluated under more realistic conditions using a computational fluid dynamics simulation of complex flow fields in a carotid bifurcation model. Afterward, in vivo clinical data on the hepatic vein were used to validate the proposed method. Finally, the accuracy of the velocity estimated by different methods was evaluated through a mean-square-error assessment. Not only could the proposed method show significant improvements using extreme small OWs, N=[2, 4] , in the simulated data in terms of frequency resolution and contrast, but it also managed to offer an improvement of 74%, 73.3%, 22.2%, and 50% in frequency resolution, and an increase of 96.5, 90.2, 49, and 31.5 dB in contrast using in vivo clinical data compared with the Capon, amplitude and phase estimation (APES), projection-based Capon, and projection-based APES, respectively, for the OW of N=4 .

United Wiener postfilter for plane wave compounding ultrasound imaging

Plane wave compounding (PWC) is a valid method for ultrafast ultrasound imaging. Its imaging quality depends on the beamforming method. The coherence factor (CF) and Wiener postfilter are effective signal processing schemes for aberration correction. However, the CF usually causes over-suppression and brings artifacts. Additionally, the conventional CF and Wiener postfilter cannot fully utilize the spatial coherence in the PWC, which limits the imaging performance and increases the computation. In this paper, we propose a united Wiener postfilter specially for the PWC. The signal and noise power are both estimated through the echo signal matrix, rather than array signal vectors. The method also accords with the theoretical relationship between the CF and Wiener. To evaluate the performance of the proposed method, we conduct simulations, phantom and in vivo experiments and make comparisons with the delay-and-sum (DAS), the CF, the generalized coherence factor (GCF), the conventional Wiener and the scaled Wiener beamformers. Results indicate that our method can offer the better resolution and contrast than the DAS and Wiener. It also solves the over-suppression drawback of the CF. Specifically, the contrast ratio and contrast-to-noise ratio achieve 26.7% and 25.2% improvements in simulations, 28.7% and 32.4% in phantom experiments, respectively. The proposed method also performs well in terms of the speckle signal-to-noise ratio and the generalized contrast-to-noise ratio. Consequently, we believe that the proposed method is effective in enhancing the imaging quality of the PWC.

Image Quality Enhancement Using a Deep Neural Network for Plane Wave Medical Ultrasound Imaging

Plane wave imaging (PWI), a typical ultrafast medical ultrasound imaging mode, adopts single plane wave emission without focusing to achieve a high frame rate. However, the imaging quality is severely degraded in comparison with the commonly used focused line scan mode. Conventional adaptive beamformers can improve imaging quality at the cost of additional computation. In this article, we propose to use a deep neural network (DNN) to enhance the performance of PWI while maintaining a high frame rate. In particular, the PWI response from a single point target is used as the network input, while the focused scan response from the same point serves as the desired output, which is the main contribution of this method. To evaluate the performance of the proposed method, simulations, phantom experiments and in vivo studies are conducted. The delay-and-sum (DAS), the coherence factor (CF), a previously proposed deep learning-based method and the DAS with focused scan are used for comparison. Numerical metrics, including the contrast ratio (CR), the contrast-to-noise ratio (CNR), and the speckle signal-to-noise ratio (sSNR), are used to quantify the performance. The results indicate that the proposed method can achieve superior resolution and contrast performance. Specifically, the proposed method performs better than the DAS in all metrics. Although the CF provides a higher CR, its CNR and sSNR are much lower than those of the proposed method. The overall performance is also better than that of the previous deep learning method and at the same level with focused scan performance. Additionally, in comparison with the DAS, the proposed method requires little additional computation, which ensures high temporal resolution. These results validate that the proposed method can achieve a high imaging quality while maintaining the high frame rate associated with PWI.

Joint Generalized Coherence Factor and Minimum Variance Beamformer for Synthetic Aperture Ultrasound Imaging

The delay-and-sum (DAS) beamformer is the most commonly used method in medical ultrasound imaging. Compared with the DAS beamformer, the minimum variance (MV) beamformer has an excellent ability to improve lateral resolution by minimizing the output of interference and noise power. However, it is hard to overcome the tradeoff between satisfactory lateral resolution and speckle preservation performance due to the fixed subarray length of covariance matrix estimation. In this study, a new approach for MV beamforming with adaptive spatial smoothing is developed to address this problem. In the new approach, the generalized coherence factor (GCF) is used as a local coherence detection tool to adaptively determine the subarray length for spatial smoothing, which is called adaptive spatial-smoothed MV (AMV). Furthermore, another adaptive regional weighting strategy based on the local signal-to-noise ratio (SNR) and GCF is devised for AMV to enhance the image contrast, which is called GCF regional weighted AMV (GAMV). To evaluate the performance of the proposed methods, we compare them with the standard MV by conducting the simulation, in vitro experiment, and the in vivo rat mammary tumor study. The results show that the proposed methods outperform MV in speckle preservation without an appreciable loss in lateral resolution. Moreover, GAMV offers excellent performance in image contrast. In particular, AMV can achieve maximal improvements of speckle signal-to-noise ratio (SNR) by 96.19% (simulation) and 62.82% (in vitro) compared with MV. GAMV achieves improvements of contrast-to-noise ratio by 27.16% (simulation) and 47.47% (in vitro) compared with GCF. Meanwhile, the losses in lateral resolution of AMV are 0.01 mm (simulation) and 0.17 mm (in vitro) compared with MV. Overall, this indicates that the proposed methods can effectively address the inherent limitation of the standard MV in order to improve the image quality.

Multi-Operator Minimum Variance Adaptive Beamforming Algorithms Accelerated With GPU

The goal of this work is to design high-resolution, high-contrast and robust MV adaptive beamforming algorithms, which are also implemented in real-time frame rate. Multi-operator optimization is introduced into MV adaptive beamforming in this work to propose a multi-operator MV adaptive beamforming algorithmic optimization framework. Based on the proposed algorithmic optimization framework, the algorithm optimization can be either conducted by activating a single optimization operator, or conducted by activating multiple optimization operators. The multi-operator MV (MOMV) adaptive beamforming algorithms are then derived from this framework. Moreover, in order to promote the real-time imaging capability of MOMV beamforming, a GPU-based parallel acceleration framework is proposed along with the algorithmic optimization framework by exploring the image-level coarse-grained parallelization and pixel-level fine-grained parallelization. GPU computing resource allocation strategy and memory access strategy are both explored to better design the acceleration framework. Comprehensive quantitative simulation evaluations and qualitative in vivo experiments of imaging performance are studied, and the results demonstrate that the proposed MOMV adaptive beamforming algorithms significantly improve the imaging performance as compared with other MV beamforming algorithms, which have high resolution, high contrast, good robustness, and real-time imaging capability with thousands of acceleration speedup. Furthermore, the MOMV beamforming algorithm without eigen-decomposition and projection optimization operator achieves much higher beamforming frame rate with little downgrade of image quality as compared with the MOMV beamforming algorithm with all optimization operators.

A unified deep network for beamforming and speckle reduction in plane wave imaging: A simulation study

Plane Wave Imaging is a fast imaging method used in ultrasound, which allows a high frame rate, but with compromised image quality when a single wave is used. In this work a learning-based approach was used to obtain improved image quality. The entire process of beamforming and speckle reduction was embedded in a single deep convolutional network, and trained with two types of simulated data. The network architecture was designed based on traditional physical considerations of the ultrasonic image formation pipe. As such, it includes beamforming with spatial matched filters, envelope detection, and a speckle reduction stage done in log-signal representation, with all stages containing trainable parameters. The approach was tested on the publicly available PICMUS datasets, achieving axial and lateral full-width-half-maximum (FWHM) resolution values of 0.22 mm and 0.35 mm respectively, and a Contrast to Noise Ratio (CNR) metric of 16.75 on the experimental datasets.

Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging

A broadband generalized sidelobe canceler (Broadband-GSC) application for near-field beamforming is proposed. This approach is implemented in the wavelet domain. Broadband-GSC provides a set of complex, adapted apodization weights for each wavelet subband. The proposed method constrains interference and noise signal to improve the lateral resolution with only one single emission. Performance of the proposed beamforming is tested on simulated data obtained with Field II. Experiments have proved that the new beamforming can significantly increase the image quality compared with delay-and-sum (DAS) and synthetic aperture (SA). Imaging of scattering points show that Broadband-GSC improves the lateral resolution by 43.2% and 58.0% compared with SA and DAS, respectively. Meanwhile，Broadband-GSC reduces the peak sidelobe level by 11.6 dB and 26.4 dB compared with SA and DAS, respectively. Plane wave emission experiment indicates that Broadband-GSC can improve the lateral resolution by 44.2% compared with DAS. Furthermore, the new beamforming introduces the possibility for higher frame-rate and higher investigation depth with increased lateral resolution.

iMAP Beamforming for High-Quality High Frame Rate Imaging

We present a statistical interpretation of beamforming to overcome the limitations of standard delay-and-sum (DAS) processing. Both the interference and the signal of interest are viewed as random variables, and the distribution of the signal of interest is exploited to maximize the a posteriori distribution of the aperture signals. In this formulation, the beamformer output is a maximum a posteriori (MAP) estimator of the signal of interest. We provide a closed-form expression for the MAP beamformer and estimate the unknown distribution parameters from the available aperture data using an empirical Bayes approach. We propose a simple scheme that iterates between the estimation of distribution parameters and the computation of the MAP estimator of the signal of interest, leading to an iterative MAP (iMAP) beamformer. This results in a suppression of the interference compared with DAS, without a severe increase in computational complexity or the need for fine-tuning of parameters. The effect of the proposed method on contrast is studied in detail and measured in terms of contrast ratio (CR), contrast-to-noise ratio (CNR), and contrast-to-speckle ratio (CSR). By implementing iMAP on both simulated and experimental data, we show that only 13 transmissions are required to obtain a CNR comparable to DAS with 75 plane waves. Compared to other interference suppression methods, such as coherence factor and scaled Wiener processing, iMAP shows an improved contrast and a better preserved speckle pattern.

A dynamic generalized coherence factor for side lobe suppression in ultrasound imaging

Coherence-based weighting techniques have been widely studied to weight beamsummed data to improve image quality in ultrasound imaging. Although generalized coherence factor (GCF) enhances the robustness of coherence factor (CF) with preserved speckle pattern by including some incoherent components, the side lobe suppression performance is insufficient due to constant cut-off frequency M₀. To address this problem, we introduced in this paper a dynamic GCF method, referred to as DGCF-C, based on the amplitude standard deviation and the convolution output of aperture data. The cut-off frequency is adaptively selected for GCF at each imaging point using the amplitude standard deviation of aperture data. Moreover, the convolution output of aperture data is used to calculate the dynamic GCF. The proposed method is evaluated in simulation and tissue-mimicking phantom studies. The image quality was analyzed in terms of resolution, contrast ratio (CR), generalized contrast-to-noise ratio (GCNR), speckle signal-to-noise ratio (sSNR), and signal-to-noise ratio (SNR). The results demonstrate that DGCF-C (M_max=2) achieves mean resolution improvements of 35.1% in simulation, and 32.6% in experiment, compared with GCF (M₀=1). Moreover, DGCF-C (M_max=4) outperforms GCF (M₀=2) with an average GCNR improvement of 13.5% and an average sSNR improvement of 15.2%, which indicates the better-preservation of speckle.

Resolving Ultrasound Contrast Microbubbles Using Minimum Variance Beamforming

Minimum Variance (MV) beamforming is known to improve the lateral resolution of ultrasound images and enhance the separation of isolated point scatterers. This paper aims to evaluate the adaptive beamformer's performance with flowing microbubbles (MBs) which are relevant to super-resolution ultrasound imaging. Simulations using point scatterer data from single emissions were complemented by an experimental investigation performed using a capillary tube phantom and the Synthetic Aperture Real-time Ultrasound System (SARUS). The MV performance was assessed by the minimum distance that allows the display of two scatterers positioned side-by-side, the lateral Full-Width-at-Half-Maximum (FWHM), and the Peak-Sidelobe-Level (PSL). In the tube, scatterer responses separated by down to [Formula: see text] (or 1.05λ ) were distinguished by the MV method, while the standard Delay-And-Sum (DAS) beamformers were unable to achieve such separation. Up to ninefold FWHM decrease was also measured in favor of the MV beamformer for individual echoes from MBs. The lateral distance between two scatterers impacted on their FWHM value, and additional differences in the scatterers' axial or out-of-plane position also impacted on their size and appearance. The simulation and experimental results were in agreement in terms of lateral resolution. The point scatterer study showed that the proposed MV imaging scheme provided clear resolution benefits compared to DAS. Current super-resolution methods mainly depend on DAS beamformers. Instead, the use of the MV method may provide a larger number of detected, and potentially better localized, MB scatterers.

2-D Minimum Variance Based Plane Wave Compounding with Generalized Coherence Factor in Ultrafast Ultrasound Imaging

Plane wave compounding (PWC) is an effective modality for ultrafast ultrasound imaging. It can provide higher resolution and better noise reduction than plane wave imaging (PWI). In this paper, a novel beamformer integrating the two-dimensional (2-D) minimum variance (MV) with the generalized coherence factor (GCF) is proposed to maintain the high resolution and contrast along with a high frame rate for PWC. To specify, MV beamforming is adopted in both the transmitting aperture and the receiving one. The subarray technique is therefore upgraded into the sub-matrix division. Then, the output of each submatrix is used to adaptively compute the GCF using a 2-D fast Fourier transform (FFT). After the 2-D MV beamforming and the 2-D GCF weighting, the final output can be obtained. Results of simulations, phantom experiments, and in vivo studies confirm the advantages of the proposed method. Compared with the delay-and-sum (DAS) beamformer, the full width at half maximum (FWHM) is 90% smaller and the contrast ratio (CR) improvement is 154% in simulations. The over-suppression of desired signals, which is a typical drawback of the coherence factor (CF), can be effectively avoided. The robustness against sound velocity errors is also enhanced.

An adaptive beamforming method for ultrasound imaging based on the mean-to-standard-deviation factor

The beamforming performance has a large impact on image quality in ultrasound imaging. Previously, several adaptive weighting factors including coherence factor (CF) and generalized coherence factor (GCF) have been proposed to improved image resolution and contrast. In this paper, we propose a new adaptive weighting factor for ultrasound imaging, which is called signal mean-to-standard-deviation factor (SMSF). SMSF is defined as the mean-to-standard-deviation of the aperture data and is used to weight the output of delay-and-sum (DAS) beamformer before image formation. Moreover, we develop a robust SMSF (RSMSF) by extending the SMSF to the spatial frequency domain using an altered spectrum of the aperture data. In addition, a square neighborhood average is applied on the RSMSF to offer a more smoothed square neighborhood RSMSF (SN-RSMSF) value. We compared our methods with DAS, CF, and GCF using simulated and experimental synthetic aperture data sets. The quantitative results show that SMSF results in an 82% lower full width at half-maximum (FWHM) but a 12% lower contrast ratio (CR) compared with CF. Moreover, the SN-RSMSF leads to 15% and 10% improvement, on average, in FWHM and CR compared with GCF while maintaining the speckle quality. This demonstrates that the proposed methods can effectively improve the image resolution and contrast.

Joint Subarray Coherence and Minimum Variance Beamformer for Multitransmission Ultrasound Imaging Modalities

Multitransmission modalities, such as plane wave compounding and synthetic aperture imaging, are promising techniques for ultrafast ultrasound imaging. Adaptive beamformers have been proposed to improve the imaging quality. Two common categories of adaptive beamformers are the minimum variance (MV)-based beamformers and the coherence factor (CF)-based beamformers. The MV can significantly improve the resolution while lacking robustness. It is also computationally expensive for multitransmission modalities. The CF can increase the contrast while over-suppressing some desired signals. In this paper, we propose a novel beamformer for better imaging quality in multitransmission ultrasound modalities. Specifically, the MV weighting process is applied to the receiving and transmitting beamforming. The spatial smoothing technique is modified for both dimensions to enhance the robustness. Then, the CF-based weights are calculated using the MV beamformed output. The submatrix technique is also used in the CF process to avoid over-suppression. Simulations and experiments are conducted to evaluate the performance of the proposed method. The results show that it can preserve the high resolution of MV and the high contrast of CF. Compared with the traditional compounding method, the full-width at half-maximum is smaller and the contrast ratio is significantly increased. Anatomic structures of an in vivo human carotid artery are more distinguishable. Because of the spatial smoothing in both dimensions, the proposed beamformer also has high robustness against the channel noise and sound velocity errors.

Eigenspace generalized sidelobe canceller combined with SNR dependent coherence factor for plane wave imaging

Background

The eigenspace generalized sidelobe canceller (EGSC) beamformer combined with a signal-to-noise ratio (SNR) dependent coherence factor (CF) is suggested for coherent plane wave compounding (PW) imaging. Conventional CF based methods such as generalized CF and subarray CF can improve the image quality, however, they are not suitable for low SNR. On the other hand, the EGSC CF based approach can introduce improvements in image quality, however, in PW imaging is susceptible to suffer from degradation due to low SNR which leads to a poor image quality. To overcome this limitation, the SNR dependent CF method is suggested for application in such situations due to its ability to control the SNR levels.

Methods

The Field II and the Verasonics ultrasound imaging system with a L11-4v array transducer with a contrast resolution phantom were used to capture the plane wave sequences of simulation and experimental data, respectively. The performance evaluation using full width at half maximum (FWHM), contrast (CR and CNR) and the speckle statistics by using the signal to noise ratio (SNR) complemented by the Rayleigh distribution analysis was performed. In order to evaluate the performance of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {EGSC}_{3}$$\end{document}EGSC3 (the SNR CF) beamformer, the comparison is done with particular importance to other CF-based approaches such as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {EGSC}_{1}$$\end{document}EGSC1 (the generalized CF) and, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {EGSC}_{2}$$\end{document}EGSC2 (the subarray CF) respectively.

Results

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Conclusion

The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {EGSC}_3$$\end{document}EGSC3 is, therefore, suitable for CPWC by improving the spatial resolution and contrast while preserving the speckle pattern.

Singular Value Decomposition-Based Generalized Side Lobe Canceller Beamforming Method for Ultrasound Imaging

In the previous studies, eigenspace-based minimum variance (ESBMV) algorithms were proposed, however, the quality of the algorithm will degrade in low signal to noise occasions. In this study, a singular value decomposition generalized side lobe canceller (SVD-GSC) beamforming method based on the GSC is proposed. The sample covariance matrix is eigendecomposed, and a kind of further SVD is introduced to establish the noise space and the signal space, respectively. After that, the weighting vectors acquired by GSC are projected into the left singular space of the desired signal space. The performance of the proposed method is investigated by both of the simulation and experimental data. And the sound velocity error is also investigated in this paper. The imaging quality of point targets are measured by the [Formula: see text][Formula: see text]dB main lobe width and the peak side lobe (PSL). The contrast ratio (CR) is introduced to describe the quality of cyst phantom. Both the point targets and cyst phantom simulation show that the proposed SVD-GSC performs better in terms of spatial resolution, PSL and CR. Furthermore, the proposed method has a stronger robustness than the traditional GSC.

A low complexity minimum variance beamformer for ultrasound imaging using dominant mode rejection

In recent years, high resolution adaptive minimum variance-based beamformers have been successfully applied to medical ultrasound imaging to improve its resolution and contrast, simultaneously. However, these improvements come at the cost of much more computational complexity in comparison to the non-adaptive delay-and-sum beamformer. The computational overhead mainly results from the L×L covariance matrix inversion needed for computation of the adaptive weights, the complexity of which is cubic with the subarray size, O(L³). In medical ultrasound imaging with focusing on the imaging point, we have a limited number of dominant modes and there is no need for the full matrix inversion. Based on this idea, we have investigated the application of the dominant mode rejection (DMR) adaptive beamformer for medical ultrasound imaging, which uses only some largest dominant modes to approximate the covariance matrix in dominant subspace. We show, using simulated and experimental data, that this subspace dimension can be selected as low as two resulting in significant computational complexity reduction while still achieving performance comparable to that of the minimum variance beamformer.

Inverse Problem of Ultrasound Beamforming With Sparsity Constraints and Regularization

Ultrasound (US) beamforming is the process of reconstructing an image from acquired echo traces on several transducer elements. Typical beamforming approaches, such as delay-and-sum, perform simple projection operations, while techniques using statistical information also exist, e.g., adaptive, phase coherence, delay-multiply-and-sum, and sparse coding approaches. Inspired by the feasibility and success of inverse problem (IP) formulations in several image reconstruction problems, such as computed tomography, we herein devise an IP approach for US beamforming. We define a linear forward model for the synthesis of the beamformed image, and solve its IP thanks to several intuitive and physics-based constraints and regularization terms proposed. These reflect the prior knowledge about the spectra of carrier signal and spatial coherence of modulated signal. These constraints admit effective formulation through sparse representations. Our proposed method was evaluated for plane-wave imaging (PWI) that transmits unfocused waves, enabling high frame rates with large field of view at the expense of much lower image quality with conventional beamforming techniques. Results are evaluated in numerical simulations, as well as tissue-mimicking phantoms and in vivo data provided by PWI challenge in medical US. The best results achieved by our proposed techniques are 0.39-mm full-width at half-maximum for spatial resolution and 16.3-dB contrast-to-noise ratio, using a single plane-wave transmit.

Experimental performance assessment of the sub-band minimum variance beamformer for ultrasound imaging

Recent progress in adaptive beamforming techniques for medical ultrasound has shown that current resolution limits can be surpassed. One method of obtaining improved lateral resolution is the Minimum Variance (MV) beamformer. The frequency domain implementation of this method effectively divides the broadband ultrasound signals into sub-bands (MVS) to conform with the narrow-band assumption of the original MV theory. This approach is investigated here using experimental Synthetic Aperture (SA) data from wire and cyst phantoms. A 7MHz linear array transducer is used with the SARUS experimental ultrasound scanner for the data acquisition. The lateral resolution and the contrast obtained, are evaluated and compared with those from the conventional Delay-and-Sum (DAS) beamformer and the MV temporal implementation (MVT). From the wire phantom the Full-Width-at-Half-Maximum (FWHM) measured at a depth of 52mm, is 16.7μm (0.08λ) for both MV methods, while the corresponding values for the DAS case are at least 24 times higher. The measured Peak-Side-lobe-Level (PSL) may reach -41dB using the MVS approach, while the values from the DAS and MVT beamforming are above -24dB and -33dB, respectively. From the cyst phantom, the power ratio (PR), the contrast-to-noise ratio (CNR), and the speckle signal-to-noise ratio (sSNR) measured at a depth of 30mm are at best similar for MVS and DAS, with values ranging between -29dB and -30dB, 1.94 and 2.05, and 2.16 and 2.27 respectively. In conclusion the MVS beamformer is not suitable for imaging continuous targets, and significant resolution gains were obtained only for isolated targets.

Generalized sidelobe canceller beamforming method for ultrasound imaging

A modified generalized sidelobe canceller (IGSC) algorithm is proposed to enhance the resolution and robustness against the noise of the traditional generalized sidelobe canceller (GSC) and coherence factor combined method (GSC-CF). In the GSC algorithm, weighting vector is divided into adaptive and non-adaptive parts, while the non-adaptive part does not block all the desired signal. A modified steer vector of the IGSC algorithm is generated by the projection of the non-adaptive vector on the signal space constructed by the covariance matrix of received data. The blocking matrix is generated based on the orthogonal complementary space of the modified steer vector and the weighting vector is updated subsequently. The performance of IGSC was investigated by simulations and experiments. Through simulations, IGSC outperformed GSC-CF in terms of spatial resolution by 0.1 mm regardless there is noise or not, as well as the contrast ratio respect. The proposed IGSC can be further improved by combining with CF. The experimental results also validated the effectiveness of the proposed algorithm with dataset provided by the University of Michigan.

Coherence factor and Wiener postfilter in synthetic aperture ultrasound imaging

The coherence factor (CF) and Wiener postfilter methods have been proposed as effective approaches for reducing the output noise of the delay-and-sum (DAS) beamformer in ultrasound imaging. The theoretical framework between them was also established. However, past researches about the CF and Wiener postfilter methods mainly focused on the summation of an array signal. This paper analyzes the CF and Wiener postfilter in the synthetic aperture (SA) imaging mode, where two-dimensional echo data are recorded. Different CF definitions in the SA imaging are first given and the corresponding Wiener postfilter methods are then proposed, including a Wiener postfilter especially for the SA imaging, named as Wiener_SA. The performances of different CF and Wiener postfilter methods were evaluated on both simulated and experimental SA data. Results showed that the proposed Wiener_SA outperformed the other Wiener postfilters in reducing the sidelobe noise level. It obtained the highest contrast ratio among the Wiener postfilter methods, which was even higher than some of the CF methods. Meanwhile it could benefit a much higher contrast-to-noise ratio than those CF methods with further suppression of incoherent noises. Consequently, the Wiener_SA is believed to be a promising approach in enhancing the SA image quality.

Ultrasound harmonic enhanced imaging using eigenspace-based coherence factor

Tissue harmonic imaging (THI) utilizes harmonic signals generating within the tissue as the result of nonlinear acoustic wave propagation. With inadequate transmitting acoustic energy, THI is incapable to detect the small objects since poor harmonic signals have been generated. In most cases, high transmission energy cannot be guaranteed because of the imaging safety issue or specific imaging modality such as the plane wave imaging (PWI). Discrimination of small point targets such as calcification, however, is particularly important in the ultrasound diagnosis. Few efforts have been made to pursue the THI with high resolution and good small target visibility at the same time. In this paper, we proposed a new eigenspace-based coherence factor (ESBCF) beamformer to solve this problem. A new kind of coherence factor (CF), named as ESBCF, is firstly proposed to detect the point targets. The detected region-of-interest (ROI) is then enhanced adaptively by using a newly developed beamforming method. The ESBCF combines the information from signal eigenspace and coherence factor by expanding the CF to the covariance matrix of signal. Analogous to the image processing but in the radio frequency (RF) data domain, the proposed method fully utilizes the information from the fundamental and harmonic components. The performance of the proposed method is demonstrated by simulation and phantom experiments. The improvement of the point contrast ratio (PCR) is 7.6dB in the simulated data, and 6.0dB in the phantom experiment. Thanks to the improved small point detection ability of the ESBCF, the proposed beamforming algorithm can enhance the PCR considerably and maintain the high resolution of the THI at the same time.

Eigenspace-based beamformer using oblique signal subspace projection for ultrasound plane-wave imaging

BACKGROUND

The Eigenspace-based beamformers, by orthogonal projection of signal subspace, can remove a large part of the noise, and provide better imaging contrast upon the minimum variance beamformer. However, wrong estimate of signal and noise component may bring dark-spot artifacts and distort the signal intensity. The signal component and noise and interference components are considered uncorrelated in conventional eigenspace-based beamforming methods. In ultrasound imaging, however, signal and noise are highly correlated. Therefore, the oblique projection instead of orthogonal projection should be taken into account in the denoising procedure of eigenspace-based beamforming algorithm.

METHODS

In this paper, we propose a novel eigenspace-based beamformer based on the oblique subspace projection that allows for consideration of the signal and noise correlation. Signal-to-interference-pulse-noise ratio and an eigen-decomposing scheme are investigated to propose a new signal and noise subspaces identification. To calculate the beamformer weights, the minimum variance weight vector is projected onto the signal subspace along the noise subspace via an oblique projection matrix.

RESULTS

We have assessed the performance of proposed beamformer by using both simulated software and real data from Verasonics system. The results have exhibited the improved imaging qualities of the proposed beamformer in terms of imaging resolution, speckle preservation, imaging contrast, and dynamic range.

CONCLUSIONS

Results have shown that, in ultrasound imaging, oblique projection is more sensible and effective than orthogonal subspace projection. Better signal and speckle preservation could be obtained by oblique projection compare to orthogonal projection. Also shadowing artifacts around the hyperechoic targets have been eliminated. Implementation the new subspace identification has enhanced the imaging resolution of the minimum variance beamformer due to the increasing the signal power in direction of arrival. Also it has offered better sidelobe suppression and a higher dynamic range.